Audience
Enterprises seeking a solution to store, share, and manage ML model features for training
About Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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Amazon SageMaker
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers.
Learn more about Amazon SageMaker
Amazon SageMaker Pipelines
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK.
Learn more about Amazon SageMaker Pipelines
Amazon SageMaker Model Deployment
Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
Learn more about Amazon SageMaker Model Deployment
Amazon SageMaker Model Building
Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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Integrations
Company Information
Amazon
Founded: 1994
United States
aws.amazon.com/sagemaker/feature-store/
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Product Details
Platforms Supported
Cloud
Training
Documentation
Webinars
Videos
Support
24/7 Live Support
Online
Amazon SageMaker Feature Store Frequently Asked Questions
Q: What kinds of users and organization types does Amazon SageMaker Feature Store work with?
Amazon SageMaker Feature Store works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does Amazon SageMaker Feature Store support in their product?
Amazon SageMaker Feature Store supports these languages: English.
Q: What kind of support options does Amazon SageMaker Feature Store offer?
Amazon SageMaker Feature Store offers support via 24/7 live support and online.
Q: What other applications or services does Amazon SageMaker Feature Store integrate with?
Amazon SageMaker Feature Store integrates with: Amazon Athena, Amazon Kinesis, Amazon Redshift, Amazon S3, Amazon SageMaker Unified Studio, Amazon SageMaker, Apache Spark, AWS Glue, AWS Lake Formation, Amazon Web Services (AWS), Databricks, Amazon SageMaker Data Wrangler, and Snowflake.
Q: What type of training does Amazon SageMaker Feature Store provide?
Amazon SageMaker Feature Store provides training in the form of documentation, webinars, and videos.
