Snowflake Unveils New AI and Data Collaboration Tools at BUILD 2024

Snowflake Strengthens Leadership In Cross-Cloud Collaboration For Enterprise Dat...

No-Headquarters/BOZEMAN, Mont. - November, 2024 - Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual developer conference, BUILD 2024, new innovations that make it easy and secure for teams to find, evaluate, and share data, AI models, and apps internally, and with external partners and customers. Through Snowflake's leading cross-cloud collaboration capabilities, users can now accelerate their AI initiatives with direct access to Snowflake's expansive ecosystem of enterprise data and AI products, while speeding up app development for end users -- all from within the security and governance boundaries of the AI Data Cloud. "To keep up with the evolving data and AI landscape, enterprises need instant access to all of the data within their organisations, supplemented with their customers' and partners' data, in order to build powerful AI apps at scale for crucial decision-making." said Prasanna Krishnan, Head of Collaboration and Horizon, Snowflake. "That's where Snowflake's industry-leading cross-cloud collaboration comes in, enabling organisations to not only access and share their most valuable data, but also apps and AI models, both internally and externally with third-parties. Snowflake's latest innovations unlock new ways for teams to collaborate on these initiatives and bring AI and apps into production faster, while handling security and governance implications to reduce risk." Building AI apps and fine-tuning models is often resource-intensive and time-consuming, and it's difficult for many enterprises to collaborate on this work across various teams without introducing significant complexity and privacy considerations. With Snowflake's Internal Marketplace (now generally available), users can easily discover available data, apps, and AI products from other teams and business units within their organisations -- while preventing unintended sharing to external parties. The Internal Marketplace also allows users to share fine-tuned large language models (LLMs) (now in public preview), making it easier for them to collaborate on generative AI use cases with increased model accuracy and performance for use case-specific tasks, and then deploy them into production. This is all done securely from within the AI Data Cloud, eliminating the need to make copies of data or transfer it between accounts. Data products listed on an organisation's Internal Marketplace can also be easily evaluated using natural language with the new Copilot for Listings (now in private preview), an AI assistant that generates and executes high-quality SQL commands and answers questions on structured data, enabling customers to quickly determine whether shared data is relevant to their work. "As a leading global mobility-tech company, it's crucial to use the full potential of data to provide our customers an unforgettable travel experience," said Tobias Hadem, Vice President of Engineering, Flix Mobility. "Snowflake's Internal Marketplace will empower our data teams to promote and securely share their data products within the organisation, driving informed, data-driven decisions across the company." In the era of AI, enterprises need speed and flexibility to build sophisticated AI-powered apps at scale. Snowflake is streamlining the app development process so customers can focus on building the apps and models that matter most to their businesses, reducing operational burden and accelerating time to production. To help organisations further build and deliver advanced apps, Snowflake is arming users with the Snowflake Native App Framework Integration with Snowpark Container Services (now generally available on AWS and public preview on Microsoft Azure). The integration allows users to easily build apps in their preferred programming language with fully customisable user experiences and then deploy them on top of configurable GPU and CPU instances, significantly enhancing the developer's time-to-value. Published apps can then be seamlessly distributed across clouds and regions, all with enhanced observability and security across the end-to-end app development process. Companies like Genesis Computing, Kumo AI, LandingAI, RelationalAI, and more are using the integration to deliver and monetise AI-powered apps to Snowflake customers through Snowflake Marketplace, unlocking new revenue streams. "We're building our business exclusively on Snowflake, and thanks to the Snowflake Native App integration with Snowpark Container Services, we were able to launch our first product in a matter of weeks," said Matthew Glickman, Co-Founder and CEO, Genesis Computing. "The integration gives us the flexibility to build sophisticated, enterprise-grade AI Data Agents at scale and quickly deploy and monetise our products on Snowflake Marketplace. Our partnership with Snowflake is providing tremendous customer value, and enabling Snowflake customers to harness the power of agentic AI in just a few clicks -- all within the security and governance boundaries of the AI Data Cloud." The Snowflake Native App Framework is also adding support for the Snowpark ML Modeling API, which uses familiar Python frameworks such as scikit-learn, LightGBM, and XGBoost for preprocessing data, feature engineering, and training models inside Snowflake. Through Snowflake's new Secure Model Sharing (now in public preview), model developers can use the Snowpark ML Modeling API to create and train models, store them in model registries within their accounts, and securely distribute and monetise these models through a Snowflake Native App on Snowflake Marketplace. To give developers more control over their data, apps, and AI product distribution costs, Snowflake is unveiling a new Egress Cost Optimiser (generally available soon) to remove incremental egress costs, or fees associated with transferring data out of a cloud service or data centre, when sharing data products to multiple cloud regions. The Egress Cost Optimiser offers developers significant cost savings, allowing them to allocate more resources to the initiatives that matter most for their businesses. In just a few clicks, enterprises can try, buy, and deploy sophisticated AI apps and data products from Snowflake Marketplace, built by providers that can easily leverage the security and governance tools provided by Snowflake. Snowflake Marketplace continues to grow, with 220+ apps and 2,500+ datasets readily available today¹ for customers to harness within seconds. For organisations with high levels of security requirements who have deployed Snowflake on Virtual Private Snowflake (VPS), or use AWS PrivateLink, Snowflake is introducing the Snowflake Native App Support for Secure Deployments (now generally available). Through this support, organisations on those secure deployments can now install and use Snowflake Native Apps. The Snowflake Native App Support for Secure Deployments enables secure data handling by reducing exposure to external threats, while helping customers maintain compliance with even the most stringent industry standards. Additionally, Snowflake Native App Compliance Badging (now private preview) allows organisations to easily identify apps that meet their internal compliance requirements like SOC2, and recognise enterprise-grade products on Snowflake Marketplace at a glance. ¹As of July 31, 2024. Forward Looking Statements This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2024 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). About Snowflake Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here.

Snowflake Expands Capabilities For Enterprises To Deliver Trustworthy AI Into Pr...

No-Headquarters/BOZEMAN, Mont. - November, 2024 - Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual developer conference, BUILD 2024, new advancements that accelerate the path for organisations to deliver easy, efficient, and trusted AI into production with their enterprise data. With Snowflake's latest innovations, developers can effortlessly build conversational apps for structured and unstructured data with high accuracy, efficiently run batch large language model (LLM) inference for natural language processing (NLP) pipelines, and train custom models with GPU-powered containers -- all with built-in governance, access controls, observability, and safety guardrails to help ensure AI security and trust remain at the forefront. "For enterprises, AI hallucinations are simply unacceptable. Today's organisations require accurate, trustworthy AI in order to drive effective decision-making, and this starts with access to high-quality data from diverse sources to power AI models," said Baris Gultekin, Head of AI, Snowflake. "The latest innovations to Snowflake Cortex AI and Snowflake ML enable data teams and developers to accelerate the path to delivering trusted AI with their enterprise data, so they can build chatbots faster, improve the cost and performance of their AI initiatives, and accelerate ML development." Snowflake Enables Enterprises to Build High-Quality Conversational Apps, Faster Thousands of global enterprises leverage Cortex AI to seamlessly scale and productionise their AI-powered apps, with adoption more than doubling¹ in just the past six months alone. Snowflake's latest innovations make it easier for enterprises to build reliable AI apps with more diverse data sources, simplified orchestration, and built-in evaluation and monitoring -- all from within Snowflake Cortex AI, Snowflake's fully managed AI service that provides a suite of generative AI features. Snowflake's advancements for end-to-end conversational app development enable customers to: Snowflake Empowers Users to Run Cost-Effective Batch LLM Inference for Natural Language Processing Batch inference allows businesses to process massive datasets with LLMs simultaneously, as opposed to the individual, one-by-one approach used for most conversational apps. In turn, NLP pipelines for batch data offer a structured approach to processing and analysing various forms of natural language data, including text, speech, and more. To help enterprises with both, Snowflake is unveiling more customisation options for large batch text processing, so data teams can build NLP pipelines with high processing speeds at scale, while optimising for both cost and performance. Snowflake is adding a broader selection of pre-trained LLMs, embedding model sizes, context window lengths, and supported languages to Cortex AI, providing organisations increased choice and flexibility when selecting which LLM to use, while maximising performance and reducing cost. This includes adding the multi-lingual embedding model from Voyage, multimodal 3.1 and 3.2 models from Llama, and huge context window models from Jamba for serverless inference. To help organisations choose the best LLM for their specific use case, Snowflake is introducing Cortex Playground (now in public preview), an integrated chat interface designed to generate and compare responses from different LLMs so users can easily find the best model for their needs. When using an LLM for various tasks at scale, consistent outputs become even more crucial to effectively understand results. As a result, Snowflake is unveiling the new Cortex Serverless Fine-Tuning (generally available soon), allowing developers to customise models with proprietary data to generate results with more accurate outputs. For enterprises that need to process large inference jobs with guaranteed throughput, the new Provisioned Throughput (public preview soon) helps them successfully do so. Customers Can Now Expedite Reliable ML with GPU-Powered Notebooks and Enhanced Monitoring Having easy access to scalable and accelerated compute significantly impacts how quickly teams can iterate and deploy models, especially when working with large datasets or using advanced deep learning frameworks. To support these compute-intensive workflows and speed up model development, Snowflake ML now supports Container Runtime (now in public preview on AWS and public preview soon on Microsoft Azure), enabling users to efficiently execute distributed ML training jobs on GPUs. Container Runtime is a fully managed container environment accessible through Snowflake Notebooks (now generally available) and preconfigured with access to distributed processing on both CPUs and GPUs. ML development teams can now build powerful ML models at scale, using any Python framework or language model of their choice, on top of their Snowflake data. "As an organisation connecting over 700,000 healthcare professionals to hospitals across the US, we rely on machine learning to accelerate our ability to place medical providers into temporary and permanent jobs. Using GPUs from Snowflake Notebooks on Container Runtime turned out to be the most cost-effective solution for our machine learning needs, enabling us to drive faster staffing results with higher success rates," said Andrew Christensen, Data Scientist, CHG Healthcare. "We appreciate the ability to take advantage of Snowflake's parallel processing with any open source library in Snowflake ML, offering flexibility and improved efficiency for our workflows." Organisations also often require GPU compute for inference. As a result, Snowflake is providing customers with new Model Serving in Containers (now public preview on AWS). This enables teams to deploy both internally and externally-trained models, including open source LLMs and embedding models, from the Model Registry into Snowpark Container Services (now generally available on AWS and Microsoft Azure) for production using distributed CPUs or GPUs -- without complex resource optimisations. In addition, users can quickly detect model degradation in production with built-in monitoring with the new Observability for ML Models (now in public preview), which integrates technology from TruEra to monitor performance and various metrics for any model running inference in Snowflake. In turn, Snowflake's new Model Explainability (now in public preview) allows users to easily compute Shapley values -- a widely-used approach that helps explain how each feature is impacting the overall output of the model -- for models logged in the Model Registry. Users can now understand exactly how a model is arriving at its final conclusion, and detect model weaknesses by noticing unintuitive behavior in production. This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. About Snowflake Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

Snowflake Unveils Snowflake Intelligence: The Future Of Data Agents For Enterprise AI

No-Headquarters/BOZEMAN, Mont. - November 12, 2024 - Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual developer conference, BUILD 2024, Snowflake Intelligence (in private preview soon), a new platform that will enable enterprises to easily ask business questions across their enterprise data to unlock data-driven answers, and in just a few steps, create data agents that take action on those insights. Snowflake Intelligence ushers in a new era for how users can interact with their enterprise data, building on Snowflake's easy, efficient, and trusted AI services. Snowflake Intelligence will give businesses enterprise-grade data agents built on Snowflake's data foundation that efficiently get organisational work done, while protecting customer IP and delivering answers backed by reliable, trusted enterprise data. Snowflake Intelligence enables everyone to easily access and harness the full potential of their data, and seamlessly connect with third-party tools -- including sales transactions in a database, documents in knowledge bases such as SharePoint, productivity tools such as Slack, Salesforce, and Google Workspace, alongside business intelligence data in Snowflake -- so they can talk to their data using natural language. Snowflake Intelligence also supports API calling to enable actions and data modifications to advance business users' work forward. "With Snowflake Intelligence, teams can easily create data agents that allow business users to talk to their enterprise data using natural language, and then analyse, summarise, and take action on those results from one unified platform," said Baris Gultekin, Head of AI, Snowflake. "Snowflake Intelligence represents the next step in Snowflake's AI journey, further enabling teams to easily, and safely, advance their businesses with data-driven insights they can act on to deliver measurable impact." Today, many organisations struggle to make informed decisions due to fragmented governance across their data sources, infrastructure silos between unstructured and structured data formats, and limited access to analysts who can write code and connect the dots across the business. Snowflake Intelligence will address all of these challenges, by first helping organisations integrate various data systems with a single governance layer. With that foundation in place, teams can then accurately process and retrieve data from both unstructured and structured data sources using natural language, truly democratising data and AI access, while empowering any user to take data-driven actions. In turn, this enables users to define the datasets they want to ask questions to, which can include PDFs or tables in a database, without having to write any code. As questions get asked, Snowflake Intelligence then puts data agents into action to do analysis, summarisation or generation tasks. Data agents can also use APIs or write to Snowflake tables to record business decisions, making it easier to track results and measure impacts. Snowflake Cortex AI and the Snowflake Horizon Catalog - the AI Engine and Governance Framework Behind Snowflake Intelligence In order to scale their AI and business needs, organisations require enterprise-grade solutions, and Snowflake Intelligence is designed to deliver on this for organisations. Snowflake Intelligence uses Snowflake Cortex AI, Snowflake's fully managed AI service that provides a suite of generative AI features, as the AI engine powering its capabilities, and was built using Cortex Search (now generally available) to run queries on unstructured data, and Cortex Analyst (in public preview) to run queries on structured data. New innovations to Cortex AI enable more users to harness Cortex AI's serverless large language model (LLM) inference, fine-tuning, retrieval augmented generation (RAG), and text-to-SQL, so data teams can process unstructured data while developers quickly build enterprise AI apps. Snowflake Intelligence also comes natively integrated with Snowflake Horizon Catalog at the foundational level, making it compatible with industry leading open formats like Apache Iceberg™ and Apache Polaris™ (Incubating). This combination delivers strong enterprise-grade compliance, security, privacy, discovery, and collaboration capabilities. This extends across clouds and regions, so organisations can spend less time worrying about implementing security and governance to protect their data, and more time driving impact. Snowflake is enhancing the Horizon Catalog so all users gain deeper capabilities to deliver secure and governed AI at scale. This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2024 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

Snowflake Strengthens Leadership in Cross-Cloud Collaboration for Enterprise Data and AI

Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual developer conference, BUILD 2024, new innovations that make it easy and secure for teams to find, evaluate, and share data, AI models, and apps internally, and with external partners and customers. Through Snowflake's leading cross-cloud collaboration capabilities, users can now accelerate their AI initiatives with direct access to Snowflake's expansive ecosystem of enterprise data and AI products, while speeding up app development for end users -- all from within the security and governance boundaries of the AI Data Cloud. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241112798682/en/ "To keep up with the evolving data and AI landscape, enterprises need instant access to all of the data within their organizations, supplemented with their customers' and partners' data, in order to build powerful AI apps at scale for crucial decision-making." said Prasanna Krishnan, Head of Collaboration and Horizon, Snowflake. "That's where Snowflake's industry-leading cross-cloud collaboration comes in, enabling organizations to not only access and share their most valuable data, but also apps and AI models, both internally and externally with third-parties. Snowflake's latest innovations unlock new ways for teams to collaborate on these initiatives and bring AI and apps into production faster, while handling security and governance implications to reduce risk." Snowflake Enables Collaboration and Sharing Within Enterprises, With Built-In Governance Building AI apps and fine-tuning models is often resource-intensive and time-consuming, and it's difficult for many enterprises to collaborate on this work across various teams without introducing significant complexity and privacy considerations. With Snowflake's Internal Marketplace (now generally available), users can easily discover available data, apps, and AI products from other teams and business units within their organizations -- while preventing unintended sharing to external parties. The Internal Marketplace also allows users to share fine-tuned large language models (LLMs) (now in public preview), making it easier for them to collaborate on generative AI use cases with increased model accuracy and performance for use case-specific tasks, and then deploy them into production. This is all done securely from within the AI Data Cloud, eliminating the need to make copies of data or transfer it between accounts. Data products listed on an organization's Internal Marketplace can also be easily evaluated using natural language with the new Copilot for Listings (now in private preview), an AI assistant that generates and executes high-quality SQL commands and answers questions on structured data, enabling customers to quickly determine whether shared data is relevant to their work. "As a leading global mobility-tech company, it's crucial to use the full potential of data to provide our customers an unforgettable travel experience," said Tobias Hadem, Vice President of Engineering, Flix Mobility. "Snowflake's Internal Marketplace will empower our data teams to promote and securely share their data products within the organization, driving informed, data-driven decisions across the company." Build, Distribute, and Monetize Data and AI Products Faster In the era of AI, enterprises need speed and flexibility to build sophisticated AI-powered apps at scale. Snowflake is streamlining the app development process so customers can focus on building the apps and models that matter most to their businesses, reducing operational burden and accelerating time to production. To help organizations further build and deliver advanced apps, Snowflake is arming users with the Snowflake Native App Framework Integration with Snowpark Container Services (now generally available on AWS and public preview on Microsoft Azure). The integration allows users to easily build apps in their preferred programming language with fully customizable user experiences and then deploy them on top of configurable GPU and CPU instances, significantly enhancing the developer's time-to-value. Published apps can then be seamlessly distributed across clouds and regions, all with enhanced observability and security across the end-to-end app development process. Companies like Genesis Computing,Kumo AI, LandingAI, RelationalAI, and more are using the integration to deliver and monetize AI-powered apps to Snowflake customers through Snowflake Marketplace, unlocking new revenue streams. "We're building our business exclusively on Snowflake, and thanks to the Snowflake Native App integration with Snowpark Container Services, we were able to launch our first product in a matter of weeks," said Matthew Glickman, Co-Founder and CEO, Genesis Computing. "The integration gives us the flexibility to build sophisticated, enterprise-grade AI Data Agents at scale and quickly deploy and monetize our products on Snowflake Marketplace. Our partnership with Snowflake is providing tremendous customer value, and enabling Snowflake customers to harness the power of agentic AI in just a few clicks -- all within the security and governance boundaries of the AI Data Cloud." The Snowflake Native App Framework is also adding support for the Snowpark ML Modeling API, which uses familiar Python frameworks such as scikit-learn, LightGBM, and XGBoost for preprocessing data, feature engineering, and training models inside Snowflake. Through Snowflake's new Secure Model Sharing (now in public preview), model developers can use the Snowpark ML Modeling API to create and train models, store them in model registries within their accounts, and securely distribute and monetize these models through a Snowflake Native App on Snowflake Marketplace. To give developers more control over their data, apps, and AI product distribution costs, Snowflake is unveiling a new Egress Cost Optimizer (generally available soon) to remove incremental egress costs, or fees associated with transferring data out of a cloud service or data center, when sharing data products to multiple cloud regions. The Egress Cost Optimizer offers developers significant cost savings, allowing them to allocate more resources to the initiatives that matter most for their businesses. Gain Direct Access to Secure, Enterprise-Grade AI and Data Products In a Just Few Clicks In just a few clicks, enterprises can try, buy, and deploy sophisticated AI apps and data products from Snowflake Marketplace, built by providers that can easily leverage the security and governance tools provided by Snowflake. Snowflake Marketplace continues to grow, with 220+ apps and 2,500+ datasets readily available today¹ for customers to harness within seconds. For organizations with high levels of security requirements who have deployed Snowflake on Virtual Private Snowflake (VPS), or use AWS PrivateLink, Snowflake is introducing the Snowflake Native App Support for Secure Deployments (now generally available). Through this support, organizations on those secure deployments can now install and use Snowflake Native Apps. The Snowflake Native App Support for Secure Deployments enables secure data handling by reducing exposure to external threats, while helping customers maintain compliance with even the most stringent industry standards. Additionally, Snowflake Native App Compliance Badging (now private preview) allows organizations to easily identify apps that meet their internal compliance requirements like SOC 2, and recognize enterprise-grade products on Snowflake Marketplace at a glance. This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2024 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). About Snowflake Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

Snowflake Build: the 4 biggest announcements on Cortex AI and more

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More At this year's annual BUILD conference, data architecture giant Snowflake went all in to give its customers advanced capabilities, including some long-previewed features, to easily mobilize their datasets to build and share powerful AI applications. The company debuted new tools for Cortex AI, its fully managed offering for developing conversational AI apps grounded in enterprise data hosted on its platform. It also announced Snowflake Intelligence, enabling users to create 'data agents' that could not only answer questions related to structured (organized in tables) and unstructured data (PDFs, documents, etc.) on the platform but also take action across third-party platforms like Salesforce and Google Workspace using the generated answers. Ever since its introduction last year, Cortex AI has been receiving regular updates from Snowflake to simplify how developers create and run AI apps. At BUILD, Snowflake continued to bolster this offering with new multimodal input support for apps in development, managed connectors to integrate internal knowledge bases to the apps, and knowledge extensions to tie third-party documents, like news articles, to the services. The company also announced Cortex Chat API combining structured and unstructured data into a single REST API call for fast-tracked RAG and agentic app development; observability for the developed AI apps (building on the TruEra acquisition); and support for SQL Joins and multi-turn conversations in Cortex Analyst to unlock richer insights from structured data. Snowflake Intelligence Using the enhancements to Cortex AI, including integration with internal knowledge bases, the company announced Snowflake Intelligence, a unified platform enterprises users can use to build 'data agents'. T The agents will use Snowflake-hosted business intelligence data as well as that connected via third-party platforms to provide users with instant answers to their business questions. Further, once the insights are produced, the users can ask the same agent to act on them across integrated third-party tools. This could involve a wide range of tasks across third party apps, from automatically and autonomously creating an editable form in Google Workspace using the generated insights to modifying an entry in Salesforce CRM. Open Catalog, Document AI enhancements Back in June, during its flagship summit, Snowflake and its industry partners unveiled Polaris as a vendor-neutral catalog implementation for indexing and organizing data conforming to the Apache Iceberg table format. The offering has since been open-sourced and donated to the Apache Foundation. At BUILD, the company took a step ahead and debuted a fully managed, hosted version of the catalog called Snowflake Open Catalog. T Now generally available, Polaris helps enterprises grow and evolve by integrating new engines and applying consistent governance controls. In addition, Snowflake also announced the general availability of Document AI, the product it offers to let users extract data from unstructured documents like invoices, on AWS and Microsoft Azure. Threat prevention and security monitoring In light of the recent customer data breach, Snowflake has taken multiple steps to bolster the security of its users, including enforcing multi-factor authentication by default. At BUILD, the company continued this work with the introduction of Leaked Password Protection, a capability that will automatically detect and notify customers if their Snowflake credentials have been exposed on the dark web (much like Google). According to Christian Kleinerman, the EVP of product at Snowflake, the company may even go and disable the accounts with compromised credentials for account protection. In addition to this, Snowflake announced a new Threat Intelligence Scanner Package for its Trust Center, the place where users see how well their accounts are configured. The feature will provide users with a risky user view, giving them the ability to detect when a potentially risky user is active along with the best steps to deal with the situation. Snowflake's Trust Center is also getting extensibility, which will enable third-party partners to leverage Snowflake's native app framework and add additional checks and assessments to the dashboard.

Snowflake adds bevy of features for AI development and managed Polaris catalog - SiliconANGLE

Snowflake adds bevy of features for AI development and managed Polaris catalog Snowflake Inc. today announced at its annual Build 2024 virtual developer conference numerous enhancements to its cloud data platform, many focused on artificial intelligence. Among them is a natural-language front-end to internal data with agentic capabilities, tools that help developers more quickly build conversational front-ends for managing and accessing structured and unstructured data, enhancements that run batch large language model inferencing more efficiently and the ability to train custom models with graphic processing unit-powered containers within Snowflake Cortex AI, a managed AI development service. Leading off the list of AI announcements is Snowflake Intelligence, a new platform soon entering private preview that's meant to enable business users to ask questions about their organization's data in natural language and to create data agents that take action on the results. Snowflake Intelligence connects with third-party tools -- including internal databases, Microsoft SharePoint document repositories, Salesforce Inc.'s customers relationship management and Slack collaboration application and Google LLC's Workspace - to combine with business intelligence data in Snowflake. The company said the toolset addresses fragmented governance across data sources, silos of unstructured and structured data and the shortage of analysts to write code to enable unified access, by replacing it with a single governance layer that accesses both unstructured and structured data sources without the need for custom coding. Data agents analyze and summarize data and to generate new tasks. They can also use application programming interfaces to read and write to Snowflake tables. Snowflake Intelligence is based on the company's Cortex AI fully managed artificial intelligence service that contains a suite of generative AI features. It also uses the Cortex Search fully managed search engine to run queries on unstructured data and Cortex Analyst to query structured data. It's natively integrated with Snowflake Horizon Catalog, making it compatible with open table formats such as Apache Iceberg and the Apache Polaris catalog. The combination delivers high levels of compliance, security, privacy, discovery and collaboration capabilities, Snowflake said. Snowflake is also releasing a managed version of the Apache Polaris catalog, which it introduced and released to open source in June. Snowflake Open Catalog, which is now generally available, allows users to integrate various engines and apply consistent governance controls across multiple table formats such as Apache Iceberg and Apache Hudi. The open-source catalog is a break from Snowflake's proprietary history. It reflects customer demand for greater choice in how they manage the large repositories called data lakes that undergird AI development. "Anyone can host Apache Polaris, but the hosted version for customers that want us to deliver a managed service is called Snowflake Open Catalog," said Christian Kleinerman, Snowflake's executive vice president of product. The second major announcement at today's conference is the general availability of the Document AI data extraction feature on the Amazon Web Services Inc. and Microsoft Corp. Azure cloud platforms. Document AI leverages Snowflake's Arctic-TILT large language model to extract and summarize information from text-heavy documents and interpret unstructured elements such as logos, handwritten text and form fills. A key distinction of Document AI is its self-learning capability, Kleinerman said. "Customers can give feedback on answers and ask Document AI to retrain or fine tune the model and continue to improve based on their feedback," he said. "Over time the model understands the use case for a given customer better and better and is exclusively trained with customer data." Business analysts and data engineers can now preprocess data in PDFs and other documents for AI training using short SQL functions for layout-aware document text extraction and text chunking functions in Cortex Search. Both features are now in public preview. The company is also unveiling a new approach to bringing transactional and analytical data together in a single platform called Unistore. It's powered by Hybrid Tables, a format that supports fast single-row operations for transactional applications. Unistore simplifies data architectures while ensuring consistent security and governance, relieving organizations of the need to manage separate transactional and analytical databases. Hybrid Tables intelligently identifies whether a query is transactional or analytical and optimizes query performance accordingly. Users can maintain application and workflow states in real time without needing to manage multiple database systems or moving between databases. This enables them to build lightweight transactional applications with Snowflake's expanded support for transactional capabilities. Snowflake has been under pressure to strengthen security since some of its customers were targeted by attackers last spring, although it said at the time that its own defenses weren't compromised but that attackers had targeted customers that weren't using multifactor authentication. Security enhancements being rolled out today include a feature in the managed Horizon Catalog that monitors the dark web and other known attacker hangouts for stolen credentials. "If we see that those match credentials that customers have in Snowflake, we will alert and potentially go all the way to disabling accounts to avoid some of the attacks we saw earlier in the year," Kleinerman said. Enhancements to the Snowflake Trust Center include a new Threat Intelligence Scanner Package, now now generally available, that automatically detects which users -- whether human or service -- are risky and recommends ways to reduce risks. Snowflake is also extending its Trust Center security framework to allow third parties to extend existing security features and sell them as Snowflake Native Applications on the Snowflake Marketplace. The feature will go into private preview soon. Support for Programmatic Access Tokens for API authentication is being added in Horizon Catalog to simplify application access while enhancing security with scope and expiration for tokens. Conversational applications are getting support for multimodal inputs with images coming first followed by audio and other data types using multimodal LLMs. Internal knowledge bases can be integrated using managed connectors such as the new Snowflake Connector for SharePoint, which is now in public preview, to automatically ingest files without to manually preprocess documents. The Cortex Chat API is being enhanced to streamline integration between the application front-end and Snowflake. Cortex Chat API combines structured and unstructured data into a single representation state transfer call for use in retrieval-augmented generation and agentic analytics. New Cortex Knowledge Extensions on Snowflake Marketplace support chat applications using unstructured data from third party content providers with isolation and attribution constructs that are meant to respect publishers' intellectual property. With AI Observability for LLM Applications, which is in private preview, users can evaluate and monitor their generative AI applications with more than 20 metrics for relevance, groundedness (the alignment of generated responses with factual, relevant and contextually accurate information), stereotype and latency during development and in production. Improvements to Cortex Analyst include simplified data analysis with advanced joins and multi-turn conversations and more dynamic retrieval with Cortex Search integration. Multi-turn conversations allow interactions between a chatbot and a user to span multiple exchanges without losing context. The features are in public preview. New customization options for large batch text processing support the construction of natural language processing pipelines at large scale. Snowflake is also adding a broader selection of pretrained LLMs, embedding model sizes, context window lengths and supported languages to Cortex AI. They include adding the multilingual embedding model from Voyage AI Innovations Inc., Meta Platforms Inc.'s multimodal Llama 3.1 and 3.2 models, and AI21 Labs Ltd.'s Jamba huge context window models for serverless inferencing. A new sandbox feature called Cortex Playground, which is now in public preview, provides an integrated chat interface where users can generate and compare responses from different LLMs. The new Cortex Serverless Fine-Tuning feature allows developers to customize models with proprietary data to generate results with more accurate outputs. Provisioned Throughput, which enters public preview soon, processes large inference jobs with guaranteed throughput. Snowflake ML, an integrated set of capabilities for machine learning development and inferencing, now supports Container Runtime in a public preview on AWS and in a forthcoming public preview on Azure. This enables more efficient execution of distributed machine learning training jobs on GPUs using any Python framework or language model. Model Serving in Containers, a feature entering public preview on AWS, enables teams to deploy both internally and externally trained models from the Snowflake Model Registry into Snowpark Container Services using distributed CPUs or GPUs. Snowpark Container Services is a managed offering that enables users to deploy, manage and scale containerized applications directly within the Snowflake ecosystem. New Storage Lifecycle Policies, now in private preview, reduce storage costs and enhance compliance by introducing new ways to archive or delete data. Snowflake is also enhancing support for data migration from relational database management systems by adding additional views support to its SnowConvert native code conversion tooling. Snowflake's Internal Marketplace, which is now generally available, enables users to discover data, applications and AI products from other teams and business units within their organizations while preventing unintended sharing with external parties. The Internal Marketplace also allows users to share fine-tuned large language models to make it easier for them to collaborate on generative AI use cases for use case-specific tasks. The function, which is now in public preview, works securely from within the AI Data Cloud, eliminating the need to make copies of data or transfer it between accounts. A new Copilot for Listings feature in private preview allows data products listed on an organization's Internal Marketplace to be easily evaluated using natural language. The AI assistant generates and executes high-quality SQL commands and answers questions that help users quickly determine whether shared data is relevant to their work. Snowflake Native Application Framework Integration with Snowpark Container Services, now generally available on AWS and in public preview on Azure, allows users to easily build applications in their preferred programming language with customizable user experiences and deploy them on top of configurable GPU and CPU instances. Published applications can be distributed across clouds and regions with observability and security across the development process. The Snowflake Native Application Framework is also adding support for the Snowpark ML Modeling API, which uses Python frameworks such as scikit-learn, LightGBM, and XGBoost for preprocessing data, feature engineering and training models inside Snowflake. New Secure Model Sharing capabilities now in public preview allow model developers to use the Snowpark ML Modeling API to create and train models, store them in model registries within their accounts, and securely distribute and make money from them on the Snowflake Marketplace.

Snowflake enhances data platform with new AI features By Investing.com

BOZEMAN, Mont. - Snowflake Inc . (NYSE: NYSE:SNOW), a leader in cloud data warehousing and analytics services, has announced the general availability of several new features designed to simplify data management and bolster security for its enterprise customers. This announcement was made at Snowflake's annual developer conference, BUILD 2024. The company revealed its Snowflake Open Catalog, now generally available, which supports the integration of new engines and the application of consistent governance controls, aiding enterprises in managing their evolving data architecture needs. Snowflake Open Catalog is built to work in tandem with the Snowflake Horizon Catalog, providing customers with flexibility and control over their data. In addition to the Open Catalog, Snowflake introduced Document AI, a tool now available on AWS and Microsoft (NASDAQ:MSFT) Azure platforms that employs a large language model, Arctic-TILT, to extract valuable insights from unstructured data such as PDFs and other documents. This feature aims to help businesses analyze text-heavy content and recognize elements like logos and handwritten notes more effectively. Snowflake also announced new threat prevention and security monitoring capabilities for the Snowflake Horizon Catalog. These enhancements include Leaked Password Protection, which automatically disables user passwords found on the dark web, and the upcoming introduction of Programmatic Access Tokens for secure API authentication. Furthermore, Snowflake is improving its platform's economics with the introduction of Storage Lifecycle Policies in private preview, which will assist organizations in managing storage costs and compliance. The company continues to simplify migrations from legacy relational database management systems with added Views support in SnowConvert. The Snowflake Trust Center is receiving an upgrade with the new Threat Intelligence Scanner Package, now generally available, that provides a Risky User View to identify and mitigate security risks. The Trust Center will also be expanded through custom scanner packages soon available on the Snowflake Marketplace from cybersecurity partners. These announcements underscore Snowflake's commitment to providing a secure and flexible data management platform for enterprises, enabling them to harness the power of AI and accelerate value from their data. This news is based on a press release statement from Snowflake Inc. In other recent news, Snowflake Inc. reported a notable 30% year-over-year increase in product revenue, reaching $829 million for its second quarter of fiscal year 2025. Following this robust performance, the company raised its full-year product revenue outlook. In addition, Snowflake recently completed a significant $2.3 billion convertible debt offering. KeyBanc Capital Markets adjusted its outlook on Snowflake shares, reducing the price target to $150 while retaining an Overweight rating. This adjustment follows a comprehensive survey of Snowflake partners and customers. Analyst firm Monness Crespi Hardt upgraded Snowflake from Neutral to Buy, setting a new price target of $140.00. Other firms such as Truist Securities, Citi, Stifel, and TD Cowen maintained their Buy ratings, with price targets ranging from $165 to $210. These are the recent developments for Snowflake, indicating a continued commitment to improved execution and quicker product innovation. As Snowflake Inc. (NYSE: SNOW) continues to innovate and expand its cloud data services, investors may be interested in some key financial metrics and insights provided by InvestingPro. Snowflake's market capitalization stands at $41.4 billion, reflecting its significant presence in the cloud computing industry. The company's revenue growth remains strong, with a 31.21% increase over the last twelve months, reaching $3.21 billion. This aligns with Snowflake's continued product development and expansion of services announced at BUILD 2024. However, profitability remains a challenge for Snowflake. An InvestingPro Tip notes that the company is not profitable over the last twelve months, with an operating income margin of -38.89%. Despite this, analysts predict that Snowflake will become profitable this year, which could be a positive sign for investors considering the company's growth trajectory and recent product announcements. Another InvestingPro Tip highlights that Snowflake holds more cash than debt on its balance sheet. This strong financial position may provide the company with the flexibility to continue investing in innovative features like the Open Catalog and Document AI, potentially driving future growth. For those interested in a deeper analysis, InvestingPro offers 7 additional tips for Snowflake, providing a more comprehensive view of the company's financial health and market position.

Snowflake's 'data agents' leverage enterprise apps so you don't have to

Set to launch in private preview soon, Snowflake Intelligence is a platform that will help enterprise users set up and deploy dedicated 'data agents' to extract relevant business insights from their data, hosted within their data cloud instance and beyond, and then use those insights to take actions across different tools and applications, like Google Workspace and Salesforce. The move comes as the rise of AI agents continues to be a prominent theme in the enterprise technology landscape, with both nimble startups and large-scale enterprises (like Salesforce) adopting them. It will further strengthen Snowflake's position in the data domain, leaving the ball in rival Databricks' court to come back with something bigger. However, it is important to note that Snowflake isn't the very first company to toy with the idea of AI agents for improved data operations. Other startups including Redbird, Altimate AI and Connecty AI, are also exploring with the idea of agents to help users better manage and extract value (in the form of AI and analytical applications) from their datasets. One key benefit of Snowflake's is that the agent creation and deployment platform will live within the same cloud data warehouse or lakehouse provider, eliminating the need for another tool. What to expect from Snowflake's data agents? Ever since Neeva AI CEO Sridhar Ramaswamy took over as CEO, Snowflake has been integrating AI capabilities on top of its core data platform to help customers take advantage of all their datasets, without running into technical complexities. From the Document AI feature launched last year to help teams extract data from their unstructured documents and to fully-managed open LLM solution Cortex AI to Snowflake Copilot, an assistant built with Cortex to write SQL queries in natural language and extract insights from data, Snowflake has been busy adding such AI features. However, until now, the AI smarts were only limited to working with the data hosted within users' respective Snowflake instances, not other sources. How Snowflake Intelligence data agents work With the launch of Snowflake Intelligence, the company is expanding these capabilities, giving teams the option to set up enterprise-grade data agents that could tap not only business intelligence data stored in their Snowflake instance, but also structured and unstructured data across siloed third-party tools -- such as sales transactions in a database, documents in knowledge bases like SharePoint, information in tools like Slack, Salesforce, and Google Workspace. According to the company, the platform, underpinned by Cortex AI's capabilities, integrates different data systems with a single governance layer and then uses Cortex Analyst and Cortex Search (part of Cortex AI architecture) to deploy agents that accurately retrieve and process specific data assets from both unstructured and structured data sources to provide relevant insights. The users interact with the agents in natural language, asking business-related questions covering different subjects, while the agents identify the relevant internal and external data sources, covering data types like PDFs, tables, etc., for those subjects and run analysis and summarization jobs to provide answers. But that's not all. Once the relevant data is surfaced, the user can ask the data agents to go a step further and take specific actions around the generated insights. For instance, a user can ask their data agent to enter the surfaced insights into an editable form and upload the file to their Google Drive. The agent would immediately analyze the query, plan and make required API function calls to connect to the relevant tools and execute the task. It can even be used for writing to Snowflake tables and making data modifications. We've reached out to Snowflake with specific questions about these data agents, including the breadth of data sources they can cover and tasks they can (or cannot) execute, but have not heard from the company at the time of writing. It also remains to be seen how quickly and easily users can create and set up these data agents. For now, the company has only said it only takes a "few steps" to deploy them. Baris Gultekin, the head of AI at Snowflake says the unified platform "represents the next step in Snowflake's AI journey, further enabling teams to easily, and safely, advance their businesses with data-driven insights they can act on to deliver measurable impact." No word on widespread availability While the idea of having agents that could answer questions about business data and then take specific actions with the generated insights to do organizational work sounds very tempting, it is pertinent to note that the capability has just been announced yet. Snowflake has not given a timeline on its availability. It only says that the unified platform will go into private preview very soon. However, the competition is intensifying fast, including from AI model provider startups such as Anthropic with its new Computer Use mode, giving users more options to choose from when it comes to turning autonomous agents loose on business data, and completing tasks from a user's text prompt instructions. The company also notes that Snowflake Intelligence will be natively integrated with the company's Horizon Catalog at the foundation level, allowing users to run agents for insights right where they discover, manage and govern their data assets. It will be compatible with both Apache Iceberg and Polaris, the company added.