Cloud AI Platform Pipelines vs Tecton

Push-button installation via the Google Cloud Console; Enterprise features for running ML workloads, including pipeline versioning, automatic metadata tracking of artifacts and executions, Cloud Logging, visualization tools, and more; Seamless integration with Google Cloud managed services like BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and many others ; Many prebuilt pipeline components (pipeline steps) for ML workflows, with easy construction of your own custom components

Feature Pipelines - automatically compute and orchestrate the feature transformation process with unified batch and real-time abstractions. Tecton includes efficient pre-engineered pipelines that compute windowed aggregations on batch and real-time data with a single line of code; Feature Store - store features in an offline store to optimize for large-scale retrieval during training and an online store for low-latency retrieval during online serving. Easily generate accurate training data through a Python SDK and backfill feature data. Serve data at very high scale (over 100,000 QPS) and low latency (under 100ms) through a REST endpoint. Tecton eliminates train-serve skew by ensuring consistency across training and serving environments, and also eliminates data leakage through correct time-travel; Feature Repository - Manage features as files in a git repository using a declarative framework. Deploy features with confidence by integrating CI/CD processes and unit testing your features before deploying to production. Manage dependencies of features across models and version-control features; Monitoring - Monitor the health of feature pipelines and automatically resolve issues that could produce stale feature data. Control costs by tracking the computation and storage costs for each feature; Sharing - Discover features through an intuitive Web UI and produce new production-grade models with existing features with a single line of code. Break down silos, increase collaboration between data scientists, data engineers, and application engineers. Eliminate duplication across the ML data development cycle