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Running Machine Learning Experiments in Python

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 Stijnman
Folkert Stijnman

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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.
Additional Resources

Folkert's course: MLOps Concepts

Folkert's project Predicting Temperature in London

[COURSE] Introduction to MLflow

[SKILL TRACK] Machine Learning Fundamentals with Python

[INFOGRAPHIC] A Beginner's Guide to The Machine Learning Workflow

Темы

Data ScienceMachine LearningPythonData Scientist

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совместная сессия кодирования

Managing Machine Learning Models with MLflow

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