Course curriculum

Learn how to bring ML to production and advance through the end to end ML lifecycle. We will review the customer maturity model for MLOps, its fundamental components. We will also show how we can use a CI (Continuous integration) approach to the data science workflow to automating the training and model testing process and use CD (Continuous Deployment) to automate the testing and deployment of production-ready models.

  • 1

    MLOps in the Enterprise

    • MLOps in the Enterprise

    • MLOps in the Enterprise

Instructor

Product Manager Microsoft

Abe Omorogbe

Abe Omorogbe is a Program Manager at Microsoft. He works within the AI Platform Group specifically on Azure Machine Learning - building exciting Machine Learning tools that make Data Scientist and ML Engineers more productive.