Amazon Lightsail
The fastest path from AI concept to production with predictable cloud infrastructure.

The open-source standard for consistent ML feature serving and storage across training and production.
Feast (Feature Store) is a CNCF-incubated open-source framework designed to bridge the gap between data engineering and machine learning. As of 2026, Feast remains the industry standard for managing the operational lifecycle of ML features. It provides a unified interface for defining, storing, and serving features, ensuring that the same feature logic is applied during both model training (offline) and real-time inference (online). The architecture is decoupled, allowing it to interface with high-performance storage backends like Redis or DynamoDB for low-latency online retrieval, and data warehouses like BigQuery, Snowflake, or Redshift for historical point-in-time joins. This prevents 'training-serving skew,' one of the most common failure modes in production AI. Feast's 2026 positioning emphasizes its role as the 'connective tissue' in the modern AI stack, enabling teams to scale from a single model to thousands of production-grade features without reinventing data pipelines for every new deployment.
A central catalog that stores feature definitions and metadata, acting as the single source of truth for the entire organization.
The fastest path from AI concept to production with predictable cloud infrastructure.
The open-source multi-modal data labeling platform for high-performance AI training and RLHF.
Scalable, Kubernetes-native Hyperparameter Tuning and Neural Architecture Search for production-grade ML.
The enterprise-grade MLOps platform for automating the deployment, management, and scaling of machine learning models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Sophisticated join logic that retrieves feature values as they existed at a specific historical timestamp.
Allows for the execution of Python-based logic at request time, combining online features with request-context data.
An interface-based architecture that allows switching between AWS, GCP, Azure, and Local providers with minimal code changes.
Direct integration with stream processors like Spark Streaming and Flink to update the online store in real-time.
Built-in integration with 'Great Expectations' to validate data quality before it reaches the model.
Automated pipeline for moving data from batch-oriented offline storage to low-latency online storage.
Banks need to assess loan risk in milliseconds using both historical user data and current session data.
Registry Updated:2/7/2026
Serve the final feature vector to the scoring model.
Presenting different products based on a user's browsing history across web and mobile apps.
Identifying suspicious transactions within 50ms to prevent financial loss.