Running Machine Learning Experiments in Python

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.

Key Takeaways:

Understand the complete machine learning pipeline, from conception to production and beyond..
Learn how to form a machine learning experiment to compare versions of models.
Learn how to use MLflow to manage a machine learning experiment pipeline.

Code along with us! https://bit.ly/479XJsl

[COURSE] Folkert's course: MLOps Concepts: https://bit.ly/3SHKz1C

[PROJECT] Folkert's project Predicting Temperature in London: https://bit.ly/3FXfqPV

[SKILL TRACK] Machine Learning Fundamentals with Python: https://bit.ly/3ug3u9u

[TUTORIAL] Streamline Your Machine Learning Workflow with MLFlow: https://bit.ly/3syQhbi

[INFOGRAPHIC] A Beginner's Guide to The Machine Learning Workflow: https://bit.ly/49CfHWg