AutoMLOps automates the generation, provisioning, deployment, and monitoring of MLOps CI/CD pipelines, bridging the gap between Data Science and DevOps. It significantly reduces the time and effort required to move ML experiments into production, empowering data scientists to focus on insights.

How It Works

The service generates a containerized MLOps codebase and provides infrastructure-as-code for managing the underlying MLOps environment. It supports flexible configuration of artifact repositories, build tools, orchestration frameworks, and source code repositories. AutoMLOps automates pipeline deployment, offers model monitoring capabilities for live endpoints, and can be configured for automatic model retraining.

Quick Start & Requirements

Installation is available via pip install google-cloud-automlops or by cloning the repository and running pip install .. Key prerequisites include Python 3.7-3.10 for code generation, Google Cloud SDK (v407.0.0+) or Terraform (v1.5.6) for provisioning, and specific Python libraries like kfp>=2.0.0 for deployment. Application Default Credentials (ADC) must be configured for Google Cloud access. CI/CD integration (use_ci=True) requires git to be installed and configured.

Highlighted Details

• Tool Flexibility: Supports a wide array of choices for artifact repositories (Artifact Registry), deployment frameworks (GitHub Actions, Cloud Build), orchestration (Kubeflow Pipelines), submission services (Cloud Functions, Cloud Run), and provisioning (gcloud, Terraform).
• Automated Provisioning: Manages infrastructure setup including GCS buckets, service accounts, IAM permissions, and necessary APIs.
• Model Monitoring: Integrated capabilities for detecting data drift and skew on deployed endpoints, with options for email alerts and automated retraining triggers.
• Code Generation: Produces a structured MLOps project directory containing components, pipeline definitions, configurations, scripts, and model monitoring code.

Maintenance & Community

The project lists Sean Rastatter as Tech Lead and Tony DiLoreto as Project Manager, along with several contributing engineers. No specific community channels (e.g., Slack, Discord) or roadmap links are provided in the README.

Licensing & Compatibility

AutoMLOps is licensed under the Apache License 2.0, permitting commercial use and derivative works, subject to the license terms.

Limitations & Caveats

Several advanced features and integrations are marked as "[coming soon]". The project is explicitly stated as "not an officially supported Google product." Users must manage Google Cloud resource provisioning and associated costs.