Machine Learning Ops?

Machine Learning Ops, or ML Ops, describes the set of tools and practices required for developing, deploying and maintaining Machine Learning Projects. This covers a large operational area and the integration and use of the tools for ML Ops can be onerous due to this. There are also gaps in the workflow that can benefit from automation or additional tooling.

Creating an ML Ops Workflow

Use actions to automate Data Science and Machine Learning Workflows. Check out the community built actions for ML Ops

The actions are modular and defined as part of a workflow in the .github/workflows directory of your repo.
Using Community Driven Actions

Actions are available to help orchestrate training, testing and deploying Machine Learning models. We have curated a set of actions available for ML Operations.
Community Featured
Contributing an Action

The list of community actions is managed through the MLOps repo. Open an issue to propose a useful MLOps action or open a pull request to add your action to the list.

Community Driven Templates

Templates are repositories that can be used as scaffolding for starting a project. The MLOps community has developed a multitude of these that allow you to get started on many different flavors of cloud. Head over to the Community Templates and choose from one of the tempates to get started.
Selecting a Template

Choose one of the templates and you will be directed to the template setup page.
Choose your repository name and you Select theme on the right.
Contributing a Template

The list of community templates is managed through the MLOps repo. Open an issue to propose a useful MLOps template or open a pull request to add your action to the list.