Amazon Enhances Customer Service and Developer Tools with Generative AI

Amazon Connect Puts Generative AI to Work Improving End-to-End Customer Experiences By Investing.com

New capabilities include proactive, personalized customer outreach, improved self-service, generative AI safeguards, and manager tools for agent coaching LAS VEGAS--(BUSINESS WIRE)--At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com (NASDAQ:AMZN), Inc. company (NASDAQ: AMZN), today announced new generative AI enhancements for Amazon Connect, AWS's cloud contact center solution. These new features will further improve customer experiences by enabling more personalized, efficient, and proactive customer service. As a result, organizations can help significantly improve customer satisfaction through faster issue resolution and continuous contact center optimization, while simultaneously reducing operational costs. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241201681975/en/ With Amazon Connect, in addition to evolving customer service, we're also fundamentally reimagining how organizations build, nurture, and sustain customer relationships," said Pasquale DeMaio, vice president and general manager of Amazon Connect at AWS. By using generative AI to improve the customer experience, Amazon Connect is paving the way for a future where every customer interaction is an opportunity to delight and foster long-term loyalty. The continuous evolution of Amazon Q in Connect is giving organizations the power and flexibility needed to handle sophisticated customer service scenarios without requiring programming expertise. Strengthen customer loyalty with more personalized experiences powered by generative AI Organizations often struggle to deliver relevant customer experiences due to fragmented data across disparate systems, including separate databases for purchases, support tickets, and online interactions. This fragmentation prevents them from gaining a holistic view of their customers' journey, while also limiting their ability to launch specialized campaigns and initiate proactive outbound communications based on real-time customer events. As a result, organizations miss crucial opportunities to engage customers at just the right moments in their journey, whether through timely support, relevant offers, or proactive communication. This leads to diminished customer satisfaction and reduced loyalty, as organizations find themselves unable to meet the growing expectations of customers who anticipate seamless, relevant interactions. Amazon Connect helps solve these problems by bridging data silos, creating a unified view of each customer that organizations can use for proactively addressing needs before issues arise and conducting outbound campaigns. Now, Amazon Connect's generative AI-powered segmentation capabilities can analyze data to provide smart recommendations on engaging different groups of customers based on both real-time and historical interactions, offering a comprehensive view of customer interactions and preferences. For example, an airline might use Amazon Connect to identify frequent flyers experiencing a significant delay, then automatically offer them priority rebooking options, lounge access, or personalized compensation based on their loyalty status and past travel patterns. Amazon Connect simplifies the process of defining meaningful customer segments and delivering relevant outbound campaigns by consolidating customer journey insights from various touchpoints. Campaign managers can then use simple, conversational commands to define segments based on this rich data. This approach enables organizations to craft precisely timed incoming and outgoing communications that respond to real-time interests and events, resulting in more personalized experiences that improve customer satisfaction and loyalty. GoStudent, a leading tutorial and education technology provider, uses Amazon Connect to ensure customer call-backs are routed to the right sales representative based on previous contact history. By leveraging Amazon Connect's enhanced unified customer profiles and outbound campaign capabilities, GoStudent will expand its sales strategy to include proactive outreach alongside existing inbound operations. This combined approach is expected to increase sales representatives' daily contacts by 20% and accelerate lead-to-customer conversions. Create generative AI-powered self-service experiences with Amazon Q in Connect Consumers expect increasingly personalized, faster, and capable self-service support. Generative AI offers a promising solution to meet these expectations; however, integrating it into a contact center environment requires significant investment in multiple third-party services, infrastructure, and specialized talent. During implementation, organizations must develop custom safeguards to regulate AI-generated responses. Without proper controls, generative AI may provide inappropriate information to customers, surface information that does not resolve customer issues, or frustrate customers by asking them for the same information multiple times. Consequently, many organizations hesitate to fully embrace generative AI, missing out on potential improvements in customer experience and contact center efficiency gains. Amazon Q in Connect now features generative AI-powered capabilities to enhance self-service customer service, offering customers the same personalized responses, proactive actions, and contextual understanding it provides to agents. Organizations can quickly create, test, and improve AI-powered self-service experiences across chat and voice channels that provide tailored responses and take proactive actions. For example, when a customer asks what rebooking options are available for their flight, Amazon Connect accesses and analyzes the customer's specific information before formulating a response. This includes checking the customer's airline status (e.g., frequent flyer level), reviewing the current ticket class, and examining eligibility based on the airline's policies. Amazon Connect then uses this data to provide a tailored response, offering rebooking options that align with the customer's status, preferences, and eligibility. If appropriate and within policy guidelines, it can proceed to book a new ticket. Amazon Connect also ensures smooth handoffs to a customer service agent, when needed, by automatically transferring the conversation, providing a comprehensive summary of the interaction, sharing all relevant customer data and context gathered, and enabling the agent to continue the conversation seamlessly without requiring the customer to repeat information. To enhance the safety and reliability of generative AI deployments in contact centers, Amazon Q in Connect now includes customizable AI guardrails. These safeguards provide organizations with robust controls over AI-generated responses without the need for extensive prompt engineering. Organizations can block undesirable topics for self-service, filter harmful and inappropriate content based on their responsible AI policies, redact sensitive information to protect customer privacy, and verify model responses using contextual grounding checks. These safeguards can be selectively applied based on contact type, offering flexible control over AI interactions. By integrating these features into Amazon Q in Connect, Amazon reduces the complexity and cost associated with building custom generative AI virtual agents, while empowering organizations to confidently leverage AI in their contact centers in alignment with their unique requirements. Frontdoor, a leading provider of home warranties and digital on-demand services, is piloting Amazon Q in Connect with the intent of reducing agent training and on-boarding time. This pilot is already reducing an agent's path to proficiency by delivering agents' next best responses and actions, based on policy documents stored in Amazon S3 within the Amazon Connect agent workspace. As they learn from how agents use this technology, Frontdoor expects to expand this same support to consumer-facing, self-service interactions. Pronetx, a professional services partner for customer experience transformation, is implementing Amazon Q in Connect for a number of public sector, federal, and financial technology organizations. With Amazon Q in Connect, they have the ability to use a single engine to drive both customer-facing conversational self-service experiences and context-aware suggestions and automations for representatives. Pronetx expects Amazon Q in Connect will allow the public sector, federal, and financial technology organizations they support to focus on creating the experiences that matter most to their customers while empowering their representatives with the best decision-making information and guidance at the right time in every customer touchpoint. Empower contact center managers with generative AI-driven insights Contact center managers face significant challenges in managing customer journeys and engagement across digital and agent interactions at scale. Traditional methods of evaluating agent performance are often time-consuming and limited in scope, typically allowing managers to assess only 1%-2% of all customer interactions. This limitation makes it difficult to provide timely and comprehensive feedback, potentially introducing bias and reducing visibility into overall performance. Managers also struggle to efficiently categorize and analyze customer contacts, hindering their ability to identify trends, spot areas for improvement, and make data-driven decisions to enhance customer experiences. Without effective tools to automatically flag critical issues such as customer discontent or requests for escalation, managers often miss opportunities to address emerging problems promptly. These limitations impede an organization's ability to be more agile and adaptive in getting ahead of external and business condition changes that impact the customer experience. To address these challenges, Amazon Connect has new enhancements that help contact center managers quickly spot important trends in customer feedback and identify agent coaching insights. Managers now have tools to automatically complete 100% of agent performance evaluations against defined quality standards, aided by conversational analytics and screen recording capabilities. Managers can automatically perform and complete evaluations, access aggregated agent performance data, identify specific coaching opportunities, and help their teams develop and grow. These improvements collectively contribute to the continuous enhancement of the customer journey. For example, managers will automatically be able to identify behavioral coaching opportunities on all customer interactions, like interaction lacked empathy while delivering bad news to the customer. Amazon Connect also uses generative AI to enable managers to easily categorize contacts. Through natural language prompts, managers can automatically categorize contacts to understand call trends over time, flag calls indicating customer discontent, learn about communication breakdowns during calls, discover agent performance improvement opportunities, and more. As a result, organizations can train their staff more effectively, identify and address common customer issues faster, and improve overall customer experiences. Fujitsu, a global digital transformation partner based in Japan, has collaborated with AWS to develop a generative AI-powered approach to quality assurance (QA). Traditionally, Fujitsu's QA process could only review 4% of voice interactions and 0.5% of chat interactions. However, with Amazon Connect, Fujitsu's service desks can now auto-score 100% of interactions across both voice and chat channels without increasing human effort. This advancement allows managers to focus on higher-level strategic initiatives and enhances QA efficiency by 60%, transforming Fujitsu's QA process into a real-time, high-sample, and unbiased approach without requiring additional QA resources. Priceline, an online travel agency offering a wide range of travel-related services, uses Amazon Connect to analyze customer interactions quickly, zeroing in on problems and areas to improve the customer experience. With Amazon Connect's generative AI-powered agent performance evaluations and call summary, Priceline expects to reduce the time managers spend evaluating customer interactions. Priceline's managers have expressed enthusiasm for the system's ability to provide rich context in review notes. University of Auckland, a public research university in New Zealand, uses generative AI-powered automated evaluations to improve the efficiency and effectiveness of their quality assurance process. Since implementing this feature of Amazon Connect, the university's team of 50 staff were able to focus more on targeted feedback and coaching rather than manual reviews. This shift is significantly improving their student support services while reducing staff training time and enhancing overall service delivery. Importantly, the new system is saving the university up to 10 hours per week on the QA process, time which can now be redirected towards other pressing tasks, further boosting productivity and service quality. All of these features are generally available today. To learn more, visit: About Amazon Web Services Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom (TADAWUL:4280) of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers"including the fastest-growing startups, largest enterprises, and leading government agencies"trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

Amazon Connect Puts Generative AI to Work Improving End-to-End Customer Experiences

New capabilities include proactive, personalized customer outreach, improved self-service, generative AI safeguards, and manager tools for agent coaching At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced new generative AI enhancements for Amazon Connect, AWS's cloud contact center solution. These new features will further improve customer experiences by enabling more personalized, efficient, and proactive customer service. As a result, organizations can help significantly improve customer satisfaction through faster issue resolution and continuous contact center optimization, while simultaneously reducing operational costs. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241201681975/en/ "With Amazon Connect, in addition to evolving customer service, we're also fundamentally reimagining how organizations build, nurture, and sustain customer relationships," said Pasquale DeMaio, vice president and general manager of Amazon Connect at AWS. "By using generative AI to improve the customer experience, Amazon Connect is paving the way for a future where every customer interaction is an opportunity to delight and foster long-term loyalty. The continuous evolution of Amazon Q in Connect is giving organizations the power and flexibility needed to handle sophisticated customer service scenarios without requiring programming expertise." Strengthen customer loyalty with more personalized experiences powered by generative AI Organizations often struggle to deliver relevant customer experiences due to fragmented data across disparate systems, including separate databases for purchases, support tickets, and online interactions. This fragmentation prevents them from gaining a holistic view of their customers' journey, while also limiting their ability to launch specialized campaigns and initiate proactive outbound communications based on real-time customer events. As a result, organizations miss crucial opportunities to engage customers at just the right moments in their journey, whether through timely support, relevant offers, or proactive communication. This leads to diminished customer satisfaction and reduced loyalty, as organizations find themselves unable to meet the growing expectations of customers who anticipate seamless, relevant interactions. Amazon Connect helps solve these problems by bridging data silos, creating a unified view of each customer that organizations can use for proactively addressing needs before issues arise and conducting outbound campaigns. Now, Amazon Connect's generative AI-powered segmentation capabilities can analyze data to provide smart recommendations on engaging different groups of customers based on both real-time and historical interactions, offering a comprehensive view of customer interactions and preferences. For example, an airline might use Amazon Connect to identify frequent flyers experiencing a significant delay, then automatically offer them priority rebooking options, lounge access, or personalized compensation based on their loyalty status and past travel patterns. Amazon Connect simplifies the process of defining meaningful customer segments and delivering relevant outbound campaigns by consolidating customer journey insights from various touchpoints. Campaign managers can then use simple, conversational commands to define segments based on this rich data. This approach enables organizations to craft precisely timed incoming and outgoing communications that respond to real-time interests and events, resulting in more personalized experiences that improve customer satisfaction and loyalty. GoStudent, a leading tutorial and education technology provider, uses Amazon Connect to ensure customer call-backs are routed to the right sales representative based on previous contact history. By leveraging Amazon Connect's enhanced unified customer profiles and outbound campaign capabilities, GoStudent will expand its sales strategy to include proactive outreach alongside existing inbound operations. This combined approach is expected to increase sales representatives' daily contacts by 20% and accelerate lead-to-customer conversions. Create generative AI-powered self-service experiences with Amazon Q in Connect Consumers expect increasingly personalized, faster, and capable self-service support. Generative AI offers a promising solution to meet these expectations; however, integrating it into a contact center environment requires significant investment in multiple third-party services, infrastructure, and specialized talent. During implementation, organizations must develop custom safeguards to regulate AI-generated responses. Without proper controls, generative AI may provide inappropriate information to customers, surface information that does not resolve customer issues, or frustrate customers by asking them for the same information multiple times. Consequently, many organizations hesitate to fully embrace generative AI, missing out on potential improvements in customer experience and contact center efficiency gains. Amazon Q in Connect now features generative AI-powered capabilities to enhance self-service customer service, offering customers the same personalized responses, proactive actions, and contextual understanding it provides to agents. Organizations can quickly create, test, and improve AI-powered self-service experiences across chat and voice channels that provide tailored responses and take proactive actions. For example, when a customer asks what rebooking options are available for their flight, Amazon Connect accesses and analyzes the customer's specific information before formulating a response. This includes checking the customer's airline status (e.g., frequent flyer level), reviewing the current ticket class, and examining eligibility based on the airline's policies. Amazon Connect then uses this data to provide a tailored response, offering rebooking options that align with the customer's status, preferences, and eligibility. If appropriate and within policy guidelines, it can proceed to book a new ticket. Amazon Connect also ensures smooth handoffs to a customer service agent, when needed, by automatically transferring the conversation, providing a comprehensive summary of the interaction, sharing all relevant customer data and context gathered, and enabling the agent to continue the conversation seamlessly without requiring the customer to repeat information. To enhance the safety and reliability of generative AI deployments in contact centers, Amazon Q in Connect now includes customizable AI guardrails. These safeguards provide organizations with robust controls over AI-generated responses without the need for extensive prompt engineering. Organizations can block undesirable topics for self-service, filter harmful and inappropriate content based on their responsible AI policies, redact sensitive information to protect customer privacy, and verify model responses using contextual grounding checks. These safeguards can be selectively applied based on contact type, offering flexible control over AI interactions. By integrating these features into Amazon Q in Connect, Amazon reduces the complexity and cost associated with building custom generative AI virtual agents, while empowering organizations to confidently leverage AI in their contact centers in alignment with their unique requirements. Frontdoor, a leading provider of home warranties and digital on-demand services, is piloting Amazon Q in Connect with the intent of reducing agent training and on-boarding time. This pilot is already reducing an agent's path to proficiency by delivering agents' next best responses and actions, based on policy documents stored in Amazon S3 within the Amazon Connect agent workspace. As they learn from how agents use this technology, Frontdoor expects to expand this same support to consumer-facing, self-service interactions. Pronetx, a professional services partner for customer experience transformation, is implementing Amazon Q in Connect for a number of public sector, federal, and financial technology organizations. With Amazon Q in Connect, they have the ability to use a single engine to drive both customer-facing conversational self-service experiences and context-aware suggestions and automations for representatives. Pronetx expects Amazon Q in Connect will allow the public sector, federal, and financial technology organizations they support to focus on creating the experiences that matter most to their customers while empowering their representatives with the best decision-making information and guidance at the right time in every customer touchpoint. Empower contact center managers with generative AI-driven insights Contact center managers face significant challenges in managing customer journeys and engagement across digital and agent interactions at scale. Traditional methods of evaluating agent performance are often time-consuming and limited in scope, typically allowing managers to assess only 1%-2% of all customer interactions. This limitation makes it difficult to provide timely and comprehensive feedback, potentially introducing bias and reducing visibility into overall performance. Managers also struggle to efficiently categorize and analyze customer contacts, hindering their ability to identify trends, spot areas for improvement, and make data-driven decisions to enhance customer experiences. Without effective tools to automatically flag critical issues such as customer discontent or requests for escalation, managers often miss opportunities to address emerging problems promptly. These limitations impede an organization's ability to be more agile and adaptive in getting ahead of external and business condition changes that impact the customer experience. To address these challenges, Amazon Connect has new enhancements that help contact center managers quickly spot important trends in customer feedback and identify agent coaching insights. Managers now have tools to automatically complete 100% of agent performance evaluations against defined quality standards, aided by conversational analytics and screen recording capabilities. Managers can automatically perform and complete evaluations, access aggregated agent performance data, identify specific coaching opportunities, and help their teams develop and grow. These improvements collectively contribute to the continuous enhancement of the customer journey. For example, managers will automatically be able to identify behavioral coaching opportunities on all customer interactions, like "interaction lacked empathy while delivering bad news to the customer." Amazon Connect also uses generative AI to enable managers to easily categorize contacts. Through natural language prompts, managers can automatically categorize contacts to understand call trends over time, flag calls indicating customer discontent, learn about communication breakdowns during calls, discover agent performance improvement opportunities, and more. As a result, organizations can train their staff more effectively, identify and address common customer issues faster, and improve overall customer experiences. Fujitsu, a global digital transformation partner based in Japan, has collaborated with AWS to develop a generative AI-powered approach to quality assurance (QA). Traditionally, Fujitsu's QA process could only review 4% of voice interactions and 0.5% of chat interactions. However, with Amazon Connect, Fujitsu's service desks can now auto-score 100% of interactions across both voice and chat channels without increasing human effort. This advancement allows managers to focus on higher-level strategic initiatives and enhances QA efficiency by 60%, transforming Fujitsu's QA process into a real-time, high-sample, and unbiased approach without requiring additional QA resources. Priceline, an online travel agency offering a wide range of travel-related services, uses Amazon Connect to analyze customer interactions quickly, zeroing in on problems and areas to improve the customer experience. With Amazon Connect's generative AI-powered agent performance evaluations and call summary, Priceline expects to reduce the time managers spend evaluating customer interactions. Priceline's managers have expressed enthusiasm for the system's ability to provide rich context in review notes. University of Auckland, a public research university in New Zealand, uses generative AI-powered automated evaluations to improve the efficiency and effectiveness of their quality assurance process. Since implementing this feature of Amazon Connect, the university's team of 50 staff were able to focus more on targeted feedback and coaching rather than manual reviews. This shift is significantly improving their student support services while reducing staff training time and enhancing overall service delivery. Importantly, the new system is saving the university up to 10 hours per week on the QA process, time which can now be redirected towards other pressing tasks, further boosting productivity and service quality. All of these features are generally available today. To learn more, visit: About Amazon Web Services Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers -- including the fastest-growing startups, largest enterprises, and leading government agencies -- trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

AWS Strengthens Amazon Bedrock with Industry-First AI Safeguard, New Agent Capability, and Model Customization By Investing.com

Automated Reasoning checks, multi-agent collaboration, and Model Distillation build on the strong foundation of enterprise-grade capabilities available on Amazon Bedrock to help customers go from proof of concept to production-ready generative AI faster LAS VEGAS--(BUSINESS WIRE)--At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com (NASDAQ:AMZN), Inc. company (NASDAQ: AMZN), today announced new capabilities for Amazon Bedrock, a fully managed service for building and scaling generative artificial intelligence (AI) applications with high-performing foundation models. Today's announcements help customers prevent factual errors due to hallucinations, orchestrate multiple AI-powered agents for complex tasks, and create smaller, task-specific models that can perform similarly to a large model at a fraction of the cost and latency. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241203470182/en/ With a broad selection of models, leading capabilities that make it easier for developers to incorporate generative AI into their applications, and a commitment to security and privacy, Amazon Bedrock has become essential for customers who want to make generative AI a core part of their applications and businesses, said Dr. Swami Sivasubramanian, vice president of AI and Data at AWS. That is why we have seen Amazon Bedrock grow its customer base by 4.7x in the last year alone. Over time, as generative AI transforms more companies and customer experiences, inference will become a core part of every application. With the launch of these new capabilities, we are innovating on behalf of customers to solve some of the top challenges, like hallucinations and cost, that the entire industry is facing when moving generative AI applications to production. Automated Reasoning checks prevent factual errors due to hallucinations While models continue to advance, even the most capable ones can hallucinate, providing incorrect or misleading responses. Hallucinations remain a fundamental challenge across the industry, limiting the trust companies can place in generative AI. This is especially true for regulated industries, like healthcare, financial services, and government agencies, where accuracy is critical, and organizations need to audit to make sure models are responding appropriately. Automated Reasoning checks is the first and only generative AI safeguard that helps prevent factual errors due to hallucinations using logically accurate and verifiable reasoning. By increasing the trust that customers can place in model responses, Automated Reasoning checks opens generative AI up to new use cases where accuracy is paramount. Automated reasoning is a branch of AI that uses math to prove something is correct. It excels when dealing with problems where users need precise answers to a topic that is large and complex, and that has a well-defined set of rules or collection of knowledge about the subject. AWS has a team of world-class automated reasoning experts who have used this technology over the last decade to improve experiences across AWS, like proving that permissions and access controls are implemented correctly to enhance security or checking millions of scenarios across Amazon Simple Storage Service (S3) before deployment to ensure availability and durability remain protected. Amazon Bedrock Guardrails makes it easy for customers to apply safety and responsible AI checks to generative AI applications, allowing customers to guide models to only talk about relevant topics. Accessible through Amazon Bedrock Guardrails, Automated Reasoning checks now allows Amazon Bedrock to validate factual responses for accuracy, produce auditable outputs, and show customers exactly why a model arrived at an outcome. This increases transparency and ensures that model responses are in line with a customer's rules and policies. For example, a health insurance provider that needs to ensure its generative AI-powered customer service application responds correctly to customer questions about policies could benefit from Automated Reasoning checks. To apply them, the provider uploads their policy information, and Amazon Bedrock automatically develops the necessary rules, guiding the customers to iteratively test it to ensure the model is tuned to the right response"no automated reasoning expertise required. The insurance provider then applies the check, and as the model generates responses, Amazon Bedrock verifies them. If a response is incorrect, like getting the deductible wrong or flagging a procedure that is not covered, Amazon Bedrock suggests the correct response using information from the Automated Reasoning check. PwC, a global professional services firm, is using Automated Reasoning checks to create AI assistants and agents that are highly accurate, trustworthy, and useful to drive its clients' businesses to the leading edge. PwC incorporates Automated Reasoning checks into industry-specific solutions for clients in financial services, healthcare, and life sciences, including applications that verify AI-generated compliance content with Food and Drug Administration (FDA) and other regulatory standards. Internally, PwC employs Automated Reasoning checks to ensure that responses generated by generative AI assistants and agents are accurate and compliant with internal policies. Easily build and coordinate multiple agents to execute complex workflows As companies make generative AI a core part of their applications, they want to do more than just summarize content and power chat experiences. They also want their applications to take action. AI-powered agents can help customers' applications accomplish these actions by using a model's reasoning capabilities to break down a task, like helping with an order return or analyzing customer retention data, into a series of steps that the model can execute. Amazon Bedrock Agents makes it easy for customers to build these agents to work across a company's systems and data sources. While a single agent can be useful, more complex tasks, like performing financial analysis across hundreds or thousands of different variables, may require a large number of agents with their own specializations. However, creating a system that can coordinate multiple agents, share context across them, and dynamically route different tasks to the right agent requires specialized tools and generative AI expertise that many companies do not have available. That is why AWS is expanding Amazon Bedrock Agents to support multi-agent collaboration, empowering customers to easily build and coordinate specialized agents to execute complex workflows. Using multi-agent collaboration in Amazon Bedrock, customers can get more accurate results by creating and assigning specialized agents for specific steps of a project and accelerate tasks by orchestrating multiple agents working in parallel. For example, a financial institution could use Amazon Bedrock Agents to help carry out due diligence on a company before investing. First, the customer uses Amazon Bedrock Agents to create a series of specialized agents focused on specific tasks, like analyzing global economic factors, assessing industry trends, and reviewing the company's historical financials. After they have created all of their specialized agents, they create a supervisor agent to manage the project. The supervisor then handles the coordination, like breaking up and routing tasks to the right agents, giving specific agents access to the information they need to complete their work, and determining what actions can be processed in parallel and which need details from other tasks before the agent can move forward. Once all of the specialized agents complete their inputs, the supervisor agent pulls the information together, synthesizes the results, and develops an overall risk profile. Moody's, a global leader in credit ratings and financial insights, has chosen Amazon Bedrock multi-agent collaboration to enhance its risk analysis workflows. Moody's is leveraging Amazon Bedrock to create agents that are each assigned a specific task and given access to tailored datasets to perform its role. For example, one agent might analyze macroeconomic trends, while another evaluates company-specific risks using proprietary financial data, and a third benchmarks competitive positioning. These agents collaborate seamlessly, synthesizing their outputs into precise, actionable insights. This innovative approach enables Moody's to deliver faster, more accurate risk assessments, solidifying its reputation as a trusted authority in financial decision-making. Create smaller, faster, more cost-effective models with Model Distillation Customers today are experimenting with a wide variety of models to find the one best suited to the unique needs of their business. However, even with all the models available today, it is challenging to find one with the right mix of specific knowledge, cost, and latency. Larger models are more knowledgeable, but they take longer to respond and cost more, while small models are faster and cheaper to run, but are not as capable. Model distillation is a technique that transfers the knowledge from a large model to a small model, while retaining the small model's performance characteristics. However, doing this requires specialized machine learning (ML) expertise to work with training data, manually fine-tune the model, and adjust model weights without compromising the performance characteristics that led the customer to choose the smaller model in the first place. With Amazon Bedrock Model Distillation, any customer can now distill their own model that can be up to 500% faster and 75% less expensive to run than original models, with less than 2% accuracy loss for use cases like retrieval augmented generation (RAG). Now, customers can optimize to achieve the best combination of capabilities, accuracy, latency, and cost for their use case"no ML expertise required. With Amazon Bedrock Model Distillation, customers simply select the best model for a given use case and a smaller model from the same model family that delivers the latency their application requires at the right cost. After the customer provides sample prompts, Amazon Bedrock will do all the work to generate responses and fine-tune the smaller model, and it can even create more sample data, if needed, to complete the distillation process. This gives customers a model with the relevant knowledge and accuracy of the large model, but the speed and cost of the smaller model, making it ideal for production use cases, like real-time chat interactions. Model Distillation works with models from Anthropic, Meta (NASDAQ:META), and the newly announced Amazon Nova Models. Robin AI, which provides an AI-powered assistant that helps make complex legal processes quicker, cheaper, and more accessible, is using Model Distillation to help power high-quality legal Q&A across millions of contract clauses. Model Distillation helps Robin AI get the accuracy they need at a fraction of the cost, while faster responses provide a more fluid interaction between their customers and the assistant. Automated Reasoning checks, multi-agent collaboration, and Model Distillation are all available in preview. Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom (TADAWUL:4280) of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers"including the fastest-growing startups, largest enterprises, and leading government agencies"trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

AWS Strengthens Amazon Bedrock with Industry-First AI Safeguard, New Agent Capability, and Model Customization

Automated Reasoning checks, multi-agent collaboration, and Model Distillation build on the strong foundation of enterprise-grade capabilities available on Amazon Bedrock to help customers go from proof of concept to production-ready generative AI faster At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced new capabilities for Amazon Bedrock, a fully managed service for building and scaling generative artificial intelligence (AI) applications with high-performing foundation models. Today's announcements help customers prevent factual errors due to hallucinations, orchestrate multiple AI-powered agents for complex tasks, and create smaller, task-specific models that can perform similarly to a large model at a fraction of the cost and latency. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241203470182/en/ "With a broad selection of models, leading capabilities that make it easier for developers to incorporate generative AI into their applications, and a commitment to security and privacy, Amazon Bedrock has become essential for customers who want to make generative AI a core part of their applications and businesses," said Dr. Swami Sivasubramanian, vice president of AI and Data at AWS. "That is why we have seen Amazon Bedrock grow its customer base by 4.7x in the last year alone. Over time, as generative AI transforms more companies and customer experiences, inference will become a core part of every application. With the launch of these new capabilities, we are innovating on behalf of customers to solve some of the top challenges, like hallucinations and cost, that the entire industry is facing when moving generative AI applications to production." Automated Reasoning checks prevent factual errors due to hallucinations While models continue to advance, even the most capable ones can hallucinate, providing incorrect or misleading responses. Hallucinations remain a fundamental challenge across the industry, limiting the trust companies can place in generative AI. This is especially true for regulated industries, like healthcare, financial services, and government agencies, where accuracy is critical, and organizations need to audit to make sure models are responding appropriately. Automated Reasoning checks is the first and only generative AI safeguard that helps prevent factual errors due to hallucinations using logically accurate and verifiable reasoning. By increasing the trust that customers can place in model responses, Automated Reasoning checks opens generative AI up to new use cases where accuracy is paramount. Automated reasoning is a branch of AI that uses math to prove something is correct. It excels when dealing with problems where users need precise answers to a topic that is large and complex, and that has a well-defined set of rules or collection of knowledge about the subject. AWS has a team of world-class automated reasoning experts who have used this technology over the last decade to improve experiences across AWS, like proving that permissions and access controls are implemented correctly to enhance security or checking millions of scenarios across Amazon Simple Storage Service (S3) before deployment to ensure availability and durability remain protected. Amazon Bedrock Guardrails makes it easy for customers to apply safety and responsible AI checks to generative AI applications, allowing customers to guide models to only talk about relevant topics. Accessible through Amazon Bedrock Guardrails, Automated Reasoning checks now allows Amazon Bedrock to validate factual responses for accuracy, produce auditable outputs, and show customers exactly why a model arrived at an outcome. This increases transparency and ensures that model responses are in line with a customer's rules and policies. For example, a health insurance provider that needs to ensure its generative AI-powered customer service application responds correctly to customer questions about policies could benefit from Automated Reasoning checks. To apply them, the provider uploads their policy information, and Amazon Bedrock automatically develops the necessary rules, guiding the customers to iteratively test it to ensure the model is tuned to the right response -- no automated reasoning expertise required. The insurance provider then applies the check, and as the model generates responses, Amazon Bedrock verifies them. If a response is incorrect, like getting the deductible wrong or flagging a procedure that is not covered, Amazon Bedrock suggests the correct response using information from the Automated Reasoning check. PwC, a global professional services firm, is using Automated Reasoning checks to create AI assistants and agents that are highly accurate, trustworthy, and useful to drive its clients' businesses to the leading edge. PwC incorporates Automated Reasoning checks into industry-specific solutions for clients in financial services, healthcare, and life sciences, including applications that verify AI-generated compliance content with Food and Drug Administration (FDA) and other regulatory standards. Internally, PwC employs Automated Reasoning checks to ensure that responses generated by generative AI assistants and agents are accurate and compliant with internal policies. Easily build and coordinate multiple agents to execute complex workflows As companies make generative AI a core part of their applications, they want to do more than just summarize content and power chat experiences. They also want their applications to take action. AI-powered agents can help customers' applications accomplish these actions by using a model's reasoning capabilities to break down a task, like helping with an order return or analyzing customer retention data, into a series of steps that the model can execute. Amazon Bedrock Agents makes it easy for customers to build these agents to work across a company's systems and data sources. While a single agent can be useful, more complex tasks, like performing financial analysis across hundreds or thousands of different variables, may require a large number of agents with their own specializations. However, creating a system that can coordinate multiple agents, share context across them, and dynamically route different tasks to the right agent requires specialized tools and generative AI expertise that many companies do not have available. That is why AWS is expanding Amazon Bedrock Agents to support multi-agent collaboration, empowering customers to easily build and coordinate specialized agents to execute complex workflows. Using multi-agent collaboration in Amazon Bedrock, customers can get more accurate results by creating and assigning specialized agents for specific steps of a project and accelerate tasks by orchestrating multiple agents working in parallel. For example, a financial institution could use Amazon Bedrock Agents to help carry out due diligence on a company before investing. First, the customer uses Amazon Bedrock Agents to create a series of specialized agents focused on specific tasks, like analyzing global economic factors, assessing industry trends, and reviewing the company's historical financials. After they have created all of their specialized agents, they create a supervisor agent to manage the project. The supervisor then handles the coordination, like breaking up and routing tasks to the right agents, giving specific agents access to the information they need to complete their work, and determining what actions can be processed in parallel and which need details from other tasks before the agent can move forward. Once all of the specialized agents complete their inputs, the supervisor agent pulls the information together, synthesizes the results, and develops an overall risk profile. Moody's, a global leader in credit ratings and financial insights, has chosen Amazon Bedrock multi-agent collaboration to enhance its risk analysis workflows. Moody's is leveraging Amazon Bedrock to create agents that are each assigned a specific task and given access to tailored datasets to perform its role. For example, one agent might analyze macroeconomic trends, while another evaluates company-specific risks using proprietary financial data, and a third benchmarks competitive positioning. These agents collaborate seamlessly, synthesizing their outputs into precise, actionable insights. This innovative approach enables Moody's to deliver faster, more accurate risk assessments, solidifying its reputation as a trusted authority in financial decision-making. Create smaller, faster, more cost-effective models with Model Distillation Customers today are experimenting with a wide variety of models to find the one best suited to the unique needs of their business. However, even with all the models available today, it is challenging to find one with the right mix of specific knowledge, cost, and latency. Larger models are more knowledgeable, but they take longer to respond and cost more, while small models are faster and cheaper to run, but are not as capable. Model distillation is a technique that transfers the knowledge from a large model to a small model, while retaining the small model's performance characteristics. However, doing this requires specialized machine learning (ML) expertise to work with training data, manually fine-tune the model, and adjust model weights without compromising the performance characteristics that led the customer to choose the smaller model in the first place. With Amazon Bedrock Model Distillation, any customer can now distill their own model that can be up to 500% faster and 75% less expensive to run than original models, with less than 2% accuracy loss for use cases like retrieval augmented generation (RAG). Now, customers can optimize to achieve the best combination of capabilities, accuracy, latency, and cost for their use case -- no ML expertise required. With Amazon Bedrock Model Distillation, customers simply select the best model for a given use case and a smaller model from the same model family that delivers the latency their application requires at the right cost. After the customer provides sample prompts, Amazon Bedrock will do all the work to generate responses and fine-tune the smaller model, and it can even create more sample data, if needed, to complete the distillation process. This gives customers a model with the relevant knowledge and accuracy of the large model, but the speed and cost of the smaller model, making it ideal for production use cases, like real-time chat interactions. Model Distillation works with models from Anthropic, Meta, and the newly announced Amazon Nova Models. Robin AI, which provides an AI-powered assistant that helps make complex legal processes quicker, cheaper, and more accessible, is using Model Distillation to help power high-quality legal Q&A across millions of contract clauses. Model Distillation helps Robin AI get the accuracy they need at a fraction of the cost, while faster responses provide a more fluid interaction between their customers and the assistant. Automated Reasoning checks, multi-agent collaboration, and Model Distillation are all available in preview. Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers -- including the fastest-growing startups, largest enterprises, and leading government agencies -- trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

Amazon Q Developer Reimagines How Developers Build and Operate Software With Generative AI By Investing.com

The most capable generative AI assistant for software development now accelerates unit testing, documentation, code reviews, and operational troubleshooting, so developers can focus on creative and exciting work LAS VEGAS--(BUSINESS WIRE)--At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com (NASDAQ:AMZN), Inc. company (NASDAQ: AMZN), today announced new enhancements to Amazon Q Developer, including agents that automate unit testing, documentation, and code reviews to help developers build faster across the entire software development process, and a capability to help users address operational issues in a fraction of the time. Amazon Q Developer is the most capable generative artificial intelligence (AI)-powered assistant for software development that is available everywhere developers need it, including the AWS Management Console, through a new integrated offering with GitLab (NASDAQ:GTLB), integrated development environments (IDEs), and more. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241203644562/en/ Amazon Q Developer is fundamentally transforming how developers work and can speed up a variety of software development tasks by up to 80%, providing the highest reported code acceptance rate of any coding assistant that suggests multi-line code, code security scanning that outperforms leading publicly benchmarkable tools, and high-performing AI agents that autonomously reason and iterate to achieve complex goals, said Deepak Singh, vice president of Next (LON:NXT) Generation Developer Experience at AWS. For these reasons and more, customers are embracing Amazon Q Developer to increase developer productivity at every stage of the software development lifecycle. With today's announcements, we are automating some of the most tedious aspects of building and operating applications, removing the undifferentiated work from software development to multiply the impact of every developer. Getting better test coverage in a fraction of the time While writing unit tests is incredibly valuable to ensure that code works as intended and to catch potential issues early, developers find it tedious and time-consuming to implement tests across all of their code. This often results in developers deprioritizing complete test coverage for speed, risking costly rollbacks on deployed code and a compromised customer experience. Generative AI-powered assistants can help with this process, but it still takes time because a developer needs to guide them through each step. To reduce this burden on developers, Amazon Q Developer now automates the process of identifying and generating unit tests, helping developers get complete test coverage with significantly less effort, so they can ship more reliable code and deliver features faster. Generating tests is now simple. From the IDE, developers just type /test in the Amazon Q Developer chat window or highlight the relevant block of code, right click, and select test. Amazon Q Developer then uses its knowledge of the entire project to autonomously identify and generate tests and add those tests to the project, helping developers quickly verify that the code is working as expected. In GitLab, developers can use Amazon Q Developer with the "/q test" quick action on a merge request to automatically generate tests for the code, saving time and improving test coverage across the organization. By eliminating the vast majority of work that goes into writing unit tests, Amazon Q gives developers more time back in their day to focus on coding, while still providing the coverage they need to know that their code is high quality. Developers at companies of all sizes are using Amazon Q Developer to get better test coverage in a fraction of the time. By equipping their developers with Amazon Q Developer, Boomi, a cloud-based integration and automation platform, anticipates reducing manual testing time by 25%, achieving complete test coverage on projects 20% faster, and fixing significantly more bugs early in the development cycle"accelerating the final, human-led reviews. With Amazon Q Developer, Boomi is proactively enhancing development efficiency and code quality, saving 15% in development costs through streamlined processes. For Tata Consultancy Services, Amazon Q Developer is accelerating their entire software development lifecycle and empowering their developers to generate comprehensive, contextually-aware unit tests up to 30% faster with high accuracy, helping ensure that their code is robust, resilient, and reliable. Deloitte is cutting manual testing time by using Amazon Q Developer to automatically identify and generate unit tests, helping their developers achieve complete test coverage quicker, deliver higher quality code, and get new solutions out faster to their clients. Overall, developers at Deloitte are increasing their development speed by 30% while maintaining robust security standards. Generating and maintaining accurate, up-to-date documentation After developers write and test their code, they have to create documentation to explain how it works. However, as a project grows, keeping all the details up-to-date is a common pain point and often gets neglected, forcing developers who are new to the codebase to spend significant time figuring out how it works on their own. To remove this heavy lifting, Amazon Q Developer now automates the process of producing and updating documentation, making it easy for developers to maintain accurate, detailed information on their projects. Now developers no longer need to break their flow when writing code to tediously capture how it works, giving them more time to dedicate to working on their project. At the same time, development teams get an organization-wide boost in productivity because teammates no longer need to invest hours trying to understand what a piece of code does. They can now confidently jump into projects with more meaningful contributions. Documentation creation works from both the IDE and via the integrated offering with GitLab. Getting started is easy, as developers simply type /doc in the IDE chat to begin producing and updating README files in their repository autonomously. To accelerate their own understanding, developers can ask Amazon Q questions about how code works or use it to improve existing documentation for better readability, making it easier for their teammates to understand their code. Amazon Q Developer presents its proposed changes for the documentation, so developers can ensure the updates are accurate and align with what they intended. Genesys, a global provider of AI-powered experience orchestration, will use Amazon Q Developer to enhance the accuracy and readability of their existing documentation. They expect developers to onboard to unfamiliar code four times faster and improve collaboration across the organization. With this usage in combination with other Amazon Q Developer agentic capabilities, like automated unit testing, feature development, and code reviews, Genesys anticipates that it could boost developer productivity by more than 30%. Netsmart, an electronic health records and solutions provider, sees the automated documentation capability from Amazon Q Developer enabling their engineers to maintain accurate information on projects with much less effort and dive into projects up to a full week faster. Using Amazon Q Developer to streamline multiple aspects of their development process, Netsmart is already experiencing a 35% code suggestion acceptance rate and expects their efficiency gains to continue to grow. Deploying higher quality code with automated code reviews One of the final steps before deployment is having another developer perform a code review to check that the code adheres to their organization's quality, style, and security standards. Developers can spend days waiting for feedback and going back-and-forth on revisions, and with typically just one reviewer, there is a chance that they could miss a potentially serious issue. To streamline this process and catch more issues sooner, Amazon Q Developer now automates code reviews, helping developers get feedback when they need it, while maintaining code quality based on engineering best practices. By acting as a first reviewer, Amazon Q helps developers detect and resolve code-quality issues earlier, saving them time on future reviews. To initiate a review from the IDE, developers type /review in the chat, and Amazon Q will flag suspicious code patterns, identify open source package risks, and assess the potential impact of releasing changes to production. Amazon Q will also use the context it has from the developer's merge request to adjust its recommendations, ensuring code suggestions are consistent with their style and preferences. When developers review their merge requests, they can invoke /q review through GitLab Duo with Amazon Q to receive feedback and streamline the review process. Amazon developers at Prime Video follow rigorous code reviews to maintain the highest quality and availability standards that their customers love. With Amazon Q Developer automated code reviews ready to assist them with every line of code and merge request, developers can produce higher quality code before peer reviews, reduce rollbacks and revisions, and accelerate work cycles. As early adopters of Amazon Q Developer, Prime Video developers are already saving hours each week, with some developers accepting over 50% of all generated recommendations. Developers at communications provider BT Group can turn to Amazon Q Developer to get feedback on their code in moments, at any time of the day"enabling them to iterate at high velocity and deliver more robust and secure code. In early usage, BT developers find the code review agent valuable, as it goes beyond identifying quality and security risks by also explaining the issues and suggesting the fixes to ensure that the code works as intended. Overall, BT Group is experiencing a 37% code acceptance rate and has automated around 12% of tedious, time-consuming work within the first four months of using Amazon Q Developer. Resolving operational issues quickly Once an application is written and deployed in production, operational teams work to make sure it is performing as expected by monitoring its health, making improvements, and fixing issues. When issues occur, teams move as quickly as possible to get the application back up and running to mitigate disruptions to their customers. However, it is a trial-and-error process that can take hours of manually sifting through vast amounts of data to find and fix the issue. Leveraging over 17 years of extensive operational experience that AWS has from running the world's largest and most reliable cloud, Amazon Q Developer now helps operators and developers of all experience levels investigate and resolve operational issues across their AWS environment in a fraction of the time. As soon as an Amazon CloudWatch alarm goes off, Amazon Q Developer can automatically start investigating. Utilizing its deep knowledge of an organization's AWS resources"including information across Amazon CloudWatch, AWS CloudTrail, AWS Health, and AWS X-Ray"it can quickly sift through hundreds of thousands of data points to discover relationships between services and develop an understanding of how they work together to identify anomalies across related signals. After analyzing its findings, Amazon Q presents users with potential hypotheses for the root cause of the issue and guides users through how to fix it"a combination of capabilities that no other major cloud provider offers. Where possible, Amazon Q surfaces runbooks and, when approved by the user, can automatically execute on them. As Amazon Q Developer takes on the heavy lifting of investigations, users can address issues much quicker, saving significant time that can be used for more strategic work. Users can also initiate an investigation when checking system signals, like a latency spike or logs showing users running into an error, across the AWS Management Console by selecting Investigate or from the Amazon Q chat by asking about their AWS resources, such as, My AWS Lambda function is running slow. What is wrong with it? Throughout an investigation, Amazon Q compiles all findings, actions, and suggested next steps in Amazon CloudWatch for the team to collaborate on and learn from to prevent future issues. AWS has more operational experience and scale than any other major cloud provider, and customers are using Amazon Q Developer to get investigation insights and resolution guidance based on this expertise to operate more efficiently. Photo-management platform SmugMug will use Amazon Q Developer to automatically analyze metrics, logs, and operational events across their systems, enabling them to diagnose most issues in under 20 minutes and up to 50% faster. This improves operational efficiency by reducing manual log searches, so their team can spend less time and resources managing issues and more time building the platform to help photographers grow their digital storefronts. At Amazon, Kindle support engineers have seen 65-80% faster issue resolution while using the Amazon Q Developer operational investigation capability, helping them more quickly address the needs of customers to provide the best user experience. Amazon Music developers are using Amazon Q as a 24/7 assistant to automate investigating and identify trends across issues, helping them move faster during their on-call shifts. Early usage shows that Amazon Music is resolving issues twice as fast, so that listeners can continue to enjoy their favorite songs. Healthcare technology provider Cedar Gate Technologies is pinpointing the root cause of operational issues in about 30 minutes, compared to two hours, by using Amazon Q Developer to accelerate investigations and swiftly resolve issues so that clients across the healthcare ecosystem have continuity providing valuable care to their patients. All of these new agents are generally available in the IDE today and in preview via the new integrated offering with GitLab. The new operational capability is available in preview. Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom (TADAWUL:4280) of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers"including the fastest-growing startups, largest enterprises, and leading government agencies"trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

AWS Unveils the Next Generation of Amazon SageMaker, Delivering a Unified Platform for Data, Analytics, and AI

AWS expands its widely adopted machine learning service, combining comprehensive data, analytics, and AI capabilities At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced the next generation of Amazon SageMaker, unifying the capabilities customers need for fast SQL analytics, petabyte-scale big data processing, data exploration and integration, model development and training, and generative artificial intelligence (AI) into one integrated platform. "We are seeing a convergence of analytics and AI, with customers using data in increasingly interconnected ways -- from historical analytics to ML model training and generative AI applications," said Swami Sivasubramanian, vice president of Data and AI at AWS. "To support these workloads, many customers already use combinations of our purpose-built analytics and ML tools, such as Amazon SageMaker -- the de facto standard for working with data and building ML models -- Amazon EMR, Amazon Redshift, Amazon S3 data lakes, and AWS Glue. The next generation of SageMaker brings together these capabilities -- along with some exciting new features -- to give customers all the tools they need for data processing, SQL analytics, ML model development and training, and generative AI, directly within SageMaker." Collaborate and build faster with Amazon SageMaker Unified Studio Today, hundreds of thousands of customers use SageMaker to build, train, and deploy ML models. Many customers also rely on the comprehensive set of purpose-built analytics services from AWS to support a wide range of workloads, including SQL analytics, search analytics, big data processing, and streaming analytics. Increasingly, customers are not using these tools in isolation; rather, they are using a combination of analytics, ML, and generative AI to derive insights and power new experiences for their users. These customers would benefit from a unified environment that brings together familiar AWS tools for analytics, ML, and generative AI, along with easy access to all of their data and the ability to easily collaborate on data projects with other members of their team or organization. The next generation of SageMaker includes a new, unified studio that gives customers a single data and AI development environment where users can find and access all of the data in their organization, act on it using the best tool for the job across all types of common data use cases, and collaborate within teams and across roles to scale their data and AI initiatives. SageMaker Unified Studio brings together functionality and tools from the range of standalone "studios," query editors, and visual tools that customers enjoy today in Amazon Bedrock, Amazon EMR, Amazon Redshift, AWS Glue, and the existing SageMaker Studio. This makes it easy for customers to access and use these capabilities to discover and prepare data, author queries or code, process data, and build ML models. Amazon Q Developer assists along the way to support development tasks such as data discovery, coding, SQL generation, and data integration. For example, a user could ask Amazon Q, "What data should I use to get a better idea of product sales?" or "Generate a SQL to calculate total revenue by product category." Users can securely publish and share data, models, applications, and other artifacts with members of their team or organization, accelerating discoverability and usage of the data assets. With the Amazon Bedrock integrated development environment (IDE) in SageMaker Unified Studio, users can build and deploy generative AI applications quickly and easily using Amazon Bedrock's selection of high-performing foundation models and tools such as Agents, Guardrails, Knowledge Bases, and Flows. SageMaker Unified Studio comes with data discovery, sharing, and governance capabilities built in, so analysts, data scientists, and engineers can easily search and find the right data they need for their use case, while applying desired security controls and permissions, maintaining access control, and securing their data. NatWest Group, a leading bank in the United Kingdom serving more than 19 million customers, uses multiple tools for data engineering, SQL analytics, ML, and generative AI workloads. With SageMaker Unified Studio, NatWest Group will have a single unified environment across the organization to support these workloads and anticipates a 50% reduction in the time required for their data users to access analytics and AI capabilities, enabling them to spend less time managing multiple services and more time innovating for their customers. Meet enterprise security needs with Amazon SageMaker data and AI governance The next generation of SageMaker simplifies the discovery, governance, and collaboration of data and AI across an organization. With SageMaker Catalog, built on Amazon DataZone, administrators can define and implement consistent access policies using a single permission model with granular controls, while data workers from across teams can securely discover and access approved data and models enriched with business context metadata created by generative AI. Administrators can easily define and enforce permissions across models, tools, and data sources, while customized safeguards help make AI applications secure and compliant. Customers can also safeguard their AI models with data classification, toxicity detection, guardrails, and responsible AI policies within SageMaker. Reduce data silos and unify data with Amazon SageMaker Lakehouse Today, more than one million data lakes are built on Amazon Simple Storage Service (Amazon S3), allowing customers to centralize their data assets and derive value with AWS analytics, AI, and ML tools. Data lakes make it possible for customers to store their data as-is -- making it easy to combine data from multiple sources. Customers may have data spread across multiple data lakes, as well as a data warehouse, and would benefit from a simple way to unify all of this data. SageMaker Lakehouse provides unified access to data stored in Amazon S3 data lakes, Redshift data warehouses, and federated data sources, reducing data silos and making it easy to query data, no matter how and where it is physically stored. With this new Apache Iceberg-compatible lakehouse capability in SageMaker, customers can access and work with all of their data from within SageMaker Unified Studio, as well as with familiar AI and ML tools and query engines compatible with Apache Iceberg open standards. Now, customers can use their preferred analytics and ML tools on their data, no matter how and where it is physically stored, to support use cases including SQL analytics, ad-hoc querying, data science, ML, and generative AI. SageMaker Lakehouse provides integrated, fine-grained access controls that are consistently applied across the data in all analytics and AI tools in the lakehouse, enabling customers to define permissions once and securely share data across their organization. Roche, a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people's lives, will use SageMaker Lakehouse to unify data from Redshift and Amazon S3 data lakes, eliminating data silos, enhancing interoperability among teams, and allowing users to seamlessly leverage data without the need for costly data movement or duplicated security access controls. With SageMaker Lakehouse, Roche anticipates a 40% reduction in data processing time, allowing them to focus more on driving their business forward and less on data management. Quickly and easily access SaaS data with the new zero-ETL integrations with SaaS applications To truly leverage data across their operations, businesses need seamless access to all their data, regardless of its location. That is why AWS has invested in a zero-ETL future, where data integration is no longer a tedious, manual effort, and customers can easily get their data where they need it. This includes zero-ETL integrations for Amazon Aurora MySQL and PostgreSQL, Amazon RDS for MySQL, and Amazon DynamoDB with Amazon Redshift, which help customers quickly and easily access data from popular relational and non-relational databases in Redshift and SageMaker Lakehouse for analytics and ML. In addition to operational databases and data lakes, many customers also have critical enterprise data stored in SaaS applications and would benefit from easy access to this data for analytics and ML. The new zero-ETL integrations with SaaS applications make it easy for customers to access their data from applications such as Zendesk and SAP in SageMaker Lakehouse and Redshift for analytics and AI. This removes the need for data pipelines, which can be challenging and costly to build, complex to manage, and prone to errors that may delay access to time-sensitive insights. Zero-ETL integrations for SaaS applications incorporate best practices for full data sync, detection of incremental updates and deletes, and target merge operations. Organizations of all sizes and across industries, including Infosys, Intuit, and Woolworths, are already benefiting from AWS zero-ETL integrations to quickly and easily connect and analyze data without building and managing data pipelines. With the zero-ETL integrations for SaaS applications, for example, online real estate platform idealista will be able to simplify their data extraction and ingestion processes, eliminating the need for multiple pipelines to access data stored in third-party SaaS applications and freeing their data engineering team to focus on deriving actionable insights from data rather than building and managing infrastructure. The next generation of SageMaker is available today. SageMaker Unified Studio is currently in preview and will be made generally available soon. Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 108 Availability Zones within 34 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud. Millions of customers -- including the fastest-growing startups, largest enterprises, and leading government agencies -- trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. About Amazon Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth's Most Customer-Centric Company, Earth's Best Employer, and Earth's Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.