Tutorial instructions and data can be found here (make sure to check prior to event): github link

Bringing a machine learning project to a successful conclusion is more difficult than it may seem upfront. We have to take into consideration full machine learning lifecycle, which allows focusing not only on the development of the model but also on the production and monitoring parts. Thankfully, there are mature tools available to manage machine learning lifecycle. One of the tools for end-to-end machine learning lifecycle management is the open-source platform MLflow.

During the tutorial we are going to deep dive into main MLFlow components:

• MLflow tracking: using an API and UI to track/log/visualize machine learning experiments.
• Mlflow projects: using standardized format to package reusable data science code.
• Mlflow models: using provided tools to deploy common model types to diverse platforms.