The machine learning workflow doesn't end with making a prediction: you often want the model to be used in production. That means using MLOps techniques to make the model available, to conduct experiments to improve the performance, and to maintain it.
In this webinar, you'll use MLflow to manage a machine learning experiment pipeline. The session will cover model evaluation, hyperparameter tuning, and MLOps strategies, using a London weather dataset.
Folkert StijnmanFounder at folkert.data
Folkert provides data science and machine learning consultancy services to companies. He previously worked as a machine learning engineer at Enjins, and is the instructor of DataCamp's "MLOps Concepts" course and the "Predicting Temperature in London" project.