AutoMLOps is a service that generates, provisions, and deploys CI/CD integrated MLOps pipelines, bridging the gap between Data Science and DevOps. AutoMLOps provides a repeatable process that dramatically reduces the time required to build MLOps pipelines. The service generates a containerized MLOps codebase, provides infrastructure-as-code to provision and maintain the underlying MLOps infra, and provides deployment functionalities to trigger and run MLOps pipelines. AutoMLOps gives flexibility over the tools and technologies used in the MLOps pipelines, allowing users to choose from a wide range of options for artifact repositories, build tools, provisioning tools, orchestration frameworks, and source code repositories. AutoMLOps can be configured to either use existing infra, or provision new infra, including source code repositories for versioning the generated MLOps codebase, build configs and triggers, artifact repositories for storing docker containers, storage buckets, etc.
Features
- Install AutoMLOps from PyPI
- Examples available
- Automatic integrations empower data scientists to take their experiments to production more quickly
- Requires Python 3.7 - 3.10
- GCP Services
- APIs & IAM