Using MLflow with Databricks
MLflow in a leading framework for MLOps supporting the tracking, registry and deployment of Machine Learning models. In this course we'll teach how to use MLflow end-to-end using Azure Databricks
In this 1-day course you'll learn what MLflow is and how to use it in Azure Databricks. We'll cover
- MLflow set-up: we'll learn you how to set-up MLflow in Databricks using all best-practices
- MLflow tracking: learning you how to track and record different training runs & performance parameters during your various model runs
- Discuss deployment options of Machine Learning incl. batch, streaming, and real-time use cases and how MLflow and Azure ML fit into the mix
- MLflow deployment: learning you to register a model, deploy that model into production & update a model in production to new version including a staging phase for testing
- MLflow administration and CICD using Azure ML: learning you how to administrator MLflow rights, access and a CICD-way-of-working using Azure DevOps and Mlflow
- You are a Data Scientist interested in the cloud for data & analytic workloads
- € 625 per day
Interested to know more?
For more information, please reach out firstname.lastname@example.org and we can give you more details & practicals.
The full element61 Training schedule (incl. when which training runs) can be found here.
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