Amazon Sage Maker

Contact for Pricing

2 Likes

Twitter

Facebook

Copy Link

Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

How Amazon Sage Maker can help you:

Enable high-performance and low-cost machine learning projects.
Provide a fully managed service with tools for building, training, and deploying ML models at scale.
Support governance requirements with simplified access control and transparency.
Offer access to hundreds of pretrained models for easy deployment.

Why choose Amazon Sage Maker: Key features

Integrated development environment (IDE) with notebooks, debuggers, profilers, and pipelines.
MLOps for managing ML operations.
Tools for fine-tuning, experimenting, retraining, and deploying large models (FMs).
Support for leading ML frameworks, toolkits, and programming languages.

Who should choose Amazon Sage Maker:

Business analysts looking to innovate with machine learning.
Data scientists in need of scalable infrastructure for ML projects.
ML engineers seeking efficient workflows for deploying models.

About Amazon Sage Maker

Website

https://aws.amazon.com/sagemaker

Release Date

March 2024

Pricing

Contact for Pricing

Related fields

Related News

Amazon AI

Amazon AI is a comprehensive suite of artificial intelligence services provided by Amazon Web Services (AWS) that enables developers to build, train, and deploy machine learning and deep learning models. This glossary entry covers the key components, use cases, and internal strategies that power Amazon AI, offering a clear overview of how AWS empowers businesses with advanced AI solutions. Amazon AI is a suite of artificial intelligence services provided by Amazon Web Services (AWS) that enables developers to build, train, and deploy machine learning and deep learning models in modern applications. Amazon AI offers a variety of pre-trained services and tools designed to help organizations prepare data, build custom AI models, and deploy scalable solutions without complex infrastructure management. The suite consists of specialized services that support various AI functions including machine learning, deep learning, and data processing. Services like Amazon SageMaker empower developers and data scientists to efficiently build, train, and deploy machine learning models for predictive analytics and tailored solutions. Amazon AI leverages deep learning technologies to handle complex data inputs, enabling tasks such as image and speech recognition and advanced natural language processing. Tools such as Amazon Textract and Amazon Comprehend automate the extraction and analysis of data from diverse sources, turning raw information into actionable insights. Amazon uses its AI services internally to drive innovation and operational efficiency, demonstrating best practices in leveraging AI within a large organization. Amazon employs a flywheel strategy where ongoing innovations in AI and machine learning are continuously refined and deployed across various company functions, boosting overall performance and service quality. Internal applications include chatbots for customer service, voice recognition in products like Alexa, and optimized logistics in Amazon warehouses, showcasing the tangible benefits of integrating AI into business operations. Amazon extends its AI capabilities to external organizations by providing fully managed, scalable AI services that simplify integration into diverse applications. Strategic partnerships with companies such as C3 AI and Hugging Face enable the sharing of open source models and accelerate the development of enterprise-scale AI applications on AWS. Amazon AI is adopted across various sectors including healthcare, retail, finance, and industrial applications, where it improves customer personalization, enhances security, and drives business innovation.

Dataconomy

Wed, 19 Feb, 2:38 PM UTC

AWS enhances QuickSight's business intelligence with AI agents - SiliconANGLE

AWS enhances QuickSight's business intelligence with AI agents Amazon Web Services Inc. said today it's expanding the generative artificial intelligence capabilities of its cloud-based business intelligence service QuickSight with agentic AI. Amazon QuickSight is a platform for business users that provides users with machine learning capabilities that can reach into company data and provide spreadsheets, charts and visualizations. It includes a generative AI capability integrated with Amazon Q, an AI-powered assistant the company unveiled during re:Invent 2023. It can answer questions, provide summaries of data and complete simple tasks based on natural language queries such as generating dashboards. Today, Amazon released "Scenarios," a new way for business users to approach their data by allowing them to become a data analyst. Previously Q in QuickSight opened up opportunities for authors -- the creators who built pages and visuals -- and consumers, the users who need summaries and questions answered. The new offering opens up the opportunity to make every user an analyst who can dive deeply into data and go hands-on with spreadsheets and datasets. This capability was originally demonstrated during re:Invent last year in preview and is now generally available. "We know that every user doesn't want to go in and understand how to do regressions and complicated analysis," Jose Kunnackal, vice president of technology, data and analytics at AWS told SiliconANGLE in an interview. "But we do know that users at some point might use a spreadsheet to manipulate data to get an outcome. Scenarios in QuickSight allows a regular business user to do this by simply stating a question and in the background it does the analysis for them." In a normal interaction with QuickSight's AI assistant, a user might ask a question and have it generate a visual, such as a chart, a graph or a dashboard. Then have it refine the result by asking another question or iterating conversationally. According to Kunnackal, this method is very slow and plodding. Scenarios accelerates analysis through agentic AI, which automates complex, multistep data analysis. This means that a user can simply type in a scenario in conversational English and an AI agent will seek out data, formulate a plan and execute the necessary steps, significantly reducing effort and time on the user's part. Kunnackal highlighted that the AI agent shows its analysis process so the user can follow along and derive the path that it took. Although the AI agent is doing the deep analysis work, it's not a complete black box. This allows users to refine their approach by seeing where the agent went and how it chose to go there. For example, a user could provide "what-if" scenarios to the AI agent and it could look through proprietary company data or uploaded documents and then take a few minutes to do an analysis. These are tasks that would take a data scientist or analyst weeks or days - now reduced to minutes. Each time a user asks a question the AI agent produces a thread where the user can add follow-ups. Users can also explore multiple related or alternative inquiries from an initial question forming simultaneous threads. That allows users to avoid linear thinking in data exploration and investigate multiple paths at once, allowing for more creative outcomes, Kunnackal said. For example, a user could examine product sales performance between different regions across separate threads, the impact of price changes on revenue and potential inventory adjustments. That approach helps mimic the natural human brainstorming process of exploring many ideas at once but lets the AI do the complex analysis and deliver a clear presentation. "Scenarios is a natural progression of the work we've been doing in QuickSight," Kunnackal added. With this addition to Q in QuickSight, Kunnackal said AWS is looking to broaden deep analysis capability to more users. After unveiling the capability last year, it has been in the hands of early customers such as BMW Group and Amazon itself where it has been put to use to uncover insights in supply chain data and to analyze support ticket patterns.

SiliconANGLE

Tue, 25 Mar, 4:32 PM UTC

Building a Scalable ML Pipeline and API in AWS

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. This blog post discusses an end-to-end ML pipeline on AWS SageMaker that leverages serverless computing, event-trigger-based data processing, and external API integrations. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications. In this blog, we will walk through the architecture, explain design decisions, and examine the key AWS services used to build this system. The AWS-based ML pipeline consists of multiple components that communicate with one another seamlessly to perform model execution, data storage, processing, and API exposure. The workflow includes: The main component of the system is the ML model that runs on AWS SageMaker periodically to generate predictions. This is also called batch processing. Once SageMaker writes the output to an S3 bucket, an event-based trigger will automatically run the next steps. Why use AWS Glue for data ingestion? Now, the data stored in DynamoDB needs to be accessed by external applications. That's done using APIs. We can use an AWS Lambda function to host the API code. We integrate AWS Route 53 with the ALB to obtain a consistent API endpoint. This AWS-based ML architecture provides a scalable, automated, and efficient pipeline for running ML models, generating predictions, and serving real-time API responses. By utilizing AWS services such as SageMaker, Lambda, Glue, DynamoDB, ALB, and Route 53, the system ensures cost efficiency, high performance, and real-time data availability for downstream applications.

dzone.com

Fri, 28 Mar, 2:33 PM UTC

Mission Cloud Unveils AI-Powered Service for AWS Optimization

Mission Cloud, an AWS Premier Tier Services Partner, has launched a new AI-driven service aimed at optimizing AWS solutions. The service leverages generative AI to enhance cloud operations and reduce costs for businesses.

2 Sources

Thu, 1 Aug, 12:05 AM UTC

Amazon is upgrading its AWS AI image generator to help your business build exactly what it needs

Amazon has unveiled an upgraded version of its AI image generator, promising improved performance and new capabilities to help your business get the most out of AI. Amazon Titan Image Generator V2 includes image conditioning abilities, allowing the tool to detect and segment out specific parts of an image in order to offer new suggestions or possibilities, allowing users to "guide" the creation of an image like never before. The company says its new model can be particularly useful for generating marketing or advertising campaigns, allowing for a range of options or evolutions of a particular image to be created and evaluated. The initial version of Titan Image Generator was released at AWS re:Invent in November 2023, offering businesses the chance to create "studio-quality, realistic images" using a model which could be trained on company-specific data, cutting down on lengthy wait or development times. The upgraded model looks to build on this, allowing for fine-tuning using assets such as company logos or specific products to make sure nothing too outlandish is created. Users can also provide a reference image along with their text prompt, with the model then providing outputs that follow the specific layout or structure. Elsewhere, the model allows for automatic background removal in images containing multiple objects, and a new subject consistency tool which allows for more fine-tuning, meaning a specific subject (eg a certain object or animal) is preserved when creating new options. "With Amazon Titan Image Generator v2, you can guide image creation using reference images, edit existing visuals, remove backgrounds, generate image variations, and securely customize the model to maintain brand style and subject consistency," Channy Yun, a Principal Developer Advocate for AWS cloud, wrote in a blog post announcing the launch. "This powerful tool streamlines workflows, boosts productivity, and brings creative visions to life." The Amazon Titan Generator v2 model is available in Amazon Bedrock now, but only in the US East (N. Virginia) and US West (Oregon) AWS Regions for now, with future releases set to be announced soon.

TechRadar

Wed, 7 Aug, 6:24 PM UTC

Similar products

MLJAR

MLJAR offers advanced Data Science tools that facilitate data understanding and utilization through automation and user-friendly interfaces.

Contact for Pricing

TensorFlow

An end-to-end open source machine learning platform for everyone.

Free

SAS Visual Data Mining & Machine Learning

Harness the power of advanced analytics with SAS Visual Data Mining & Machine Learning, an integrated solution for data-driven insights.

Contact for Pricing

SAS Model Manager

SAS Model Manager streamlines your analytical modeling life cycle, from creation and validation to deployment, management, governance and monitoring.

Contact for Pricing

Saturn Cloud

Data science and machine learning in the cloud in seconds—Use R and Python with GPUs, Dask clusters, and more

Contact for Pricing

Your one-stop AI hub

The Outpost is a comprehensive collection of curated artificial intelligence software tools that cater to the needs of small business owners, bloggers, artists, musicians, entrepreneurs, marketers, writers, and researchers.

Creative and design

Amazon Sage Maker

Contact for Pricing

About Amazon Sage Maker

Related fields

Related News

Similar products

MLJAR

TensorFlow

SAS Visual Data Mining & Machine Learning

SAS Model Manager

Saturn Cloud

Your one-stop AI hub

The Outpost

Keep in touch

Subscribe to our newsletter

Amazon Sage Maker

Contact for Pricing

About Amazon Sage Maker

Related fields

Related News

Similar products

MLJAR

TensorFlow

SAS Visual Data Mining & Machine Learning

SAS Model Manager

Saturn Cloud

Your one-stop AI hub

The Outpost

Keep in touch