Google has beta released its new Cloud AI Platform Pipelines. Cloud AI Platform Pipelines allows developers to deploy robust, repeatable ML pipelines that include monitoring, auditing, version tracking, and reproducibility. Google has specifically designed the offering to be enterprise-ready, simple to install, and secure.
"When you're just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward," the company commented in a blog post announcement. "But when you need to start paying attention to the other pieces required to make a ML workflow sustainable and scalable, things become more complex. A machine learning workflow can involve many steps with dependencies on each other, from data preparation and analysis, to training, to evaluation, to deployment, and more."
Cloud AI Platform Pipelines is made up of two major components: an enterprise-ready infrastructure and pipeline tools. The infrastructure includes the architecture needed to deploy and run structured machine learning workflows that integrate with the Google Cloud Platform. The pipeline tools are used for building, debugging, and sharing various pipelines and components.
During the beta, AI Platform Pipelines includes two SDKs that will eventually merge into one. At beta launch, it includes:
- 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
Check out the docs to learn more.