What's New? - CAIP - 24-PI6
๐ What's New in CAIP after 24-PI6?โ
24-PI6 (October 22, 2024 โ January 13, 2025) improved CAIP build and scheduling reliability, extended Glue support, and marked CAIP Offboard v2's first LLM API release with authenticated model access.
๐ฅ๏ธ CAIP Offboard v1 / v1.5: Classic & Managed Kubeflowโ
โจ New Featuresโ
๐ Notebooks & Developmentโ
The CAIP SDK added Python 3.10 support for Glue and let teams open a run directly in the web interface with a run alias. Cached Docker builds shortened repeat builds, while AWS public ECR Python base images avoided Docker Hub rate limits that could otherwise delay them.
๐ Pipelines & Servingโ
Glue and EMR components became compatible with CAIP SDK 3.7.1. EMR runs also linked directly to the Grafana Spark-on-EKS dashboard, helping teams move from a pipeline run to the operational information relevant to that workload.
๐ Maintenanceโ
Cron scheduling validation was corrected, helping scheduled pipelines reject invalid expressions before they run.
๐ฆ Repositories in This Releaseโ
caip-sdkโ Glue Python 3.10 support, web run access, cached builds, scheduling validation, and public ECR base images.kubeflow-componentsโ Glue and EMR compatibility plus Grafana dashboard links.
This section covers customer-visible changes for CAIP Offboard v1/v1.5 from October 22, 2024 โ January 13, 2025 (24-PI6). For guides and reference material, see the CAIP Documentation.
๐ CAIP Offboard v2: All-New CAIPโ
โจ New Featuresโ
๐ค LLM APIโ
The LLM API had its first release, delivering authenticated model access in mcaip-inf through the AI Gateway. Both interactive users and machine-to-machine clients could authenticate with OIDC to use the available models, establishing a managed entry point to this new CAIP Offboard v2 capability.
๐ฆ Repositories in This Releaseโ
productsโ first LLM API release with authenticated AI Gateway access.
This section covers customer-visible changes for CAIP Offboard v2 from October 22, 2024 โ January 13, 2025 (24-PI6). For guides and reference material, see the LLM API Documentation.