AutoMLOps 1.0.2

Added feature to allow for accelerated/distributed model training.

Added

• Support for custom training job specs (specifies which resources to use for pipeline jobs).

Changed

• Updated readme and implementation guide.
• New custom_training_job_specs parameter.
• Changed workflow for PipelineBuilder

Fixed

• Bug related to grep substring match for create_resources script.

AutoMLOps 1.0.1

Reworked process to submit jobs to cloud runner service.

Added

• Cloud Tasks Queue; API enabling, queue creation, and task generation.

Changed

• run() workflow reduced; submission to cloud runner service now takes place as part of the cloudbuild script.
• Creation of schedule job is now part of the cloudbuild script.
• Removed submit_to_runner_svc.sh and create_scheduler.sh.
• Added support for vpc_connectors.

Fixed

• Bug related to waiting for cloudbuild job to complete before submitting to cloud runner service.
• Bug related to elevated IAM privileges in order to authenticate before submitting to cloud runner service.

AutoMLOps 1.0.0

Official release of AutoMLOps!

AutoMLOps is a service that generates a production ready MLOps pipeline from Jupyter Notebooks, bridging the gap between Data Science and DevOps and accelerating the adoption and use of Vertex AI. The service generates an MLOps codebase for users to customize, and provides a way to build and manage a CI/CD integrated MLOps pipeline from the notebook. AutoMLOps automatically builds a source repo for versioning, cloudbuild configs and triggers, an artifact registry for storing custom components, gs buckets, service accounts and updated IAM privs for running pipelines, enables APIs (cloud Run, Cloud Build, Artifact Registry, etc.), creates a runner service API in Cloud Run for submitting PipelineJobs to Vertex AI, and a Cloud Scheduler job for submitting PipelineJobs on a recurring basis. These automatic integrations empower data scientists to take their experiments to production more quickly, allowing them to focus on what they do best: providing actionable insights through data.