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.

Nov 14, 2023

Code along with us onCode Along

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.

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

Topics

Learn Topics Related to This Code-Along

Machine Learning Experimentation: An Introduction to Weights & Biases

Learn how to structure, log, and analyze your machine learning experiments using Weights & Biases.

Tutorial

Introduction to MLflow

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

Course

Understanding Machine Learning

An introduction to machine learning with no coding involved.

Course

Tutorial

Streamline Your Machine Learning Workflow with MLFlow

Take a deep dive into what MLflow is and how you can leverage this open-source platform for tracking and deploying your machine learning experiments.

Moez Ali

12 min

Tutorial

Machine Learning, Pipelines, Deployment and MLOps Tutorial

Learn basic MLOps and end-to-end development and deployment of ML pipelines.

Moez Ali

15 min

code-along

Managing Machine Learning Models with MLflow

Learn to use MLflow to track and package a machine learning model, and see the process for getting models into production.

Weston Bassler

code-along

Introduction to Experiment Tracking

This webinar shows you how to get started with experiment tracking, a pivotal yet frequently overlooked aspect of the data science lifecycle.

Jinen Setpal

code-along

Getting Started with Machine Learning in Python

Learn the fundamentals of supervised learning by using scikit-learn.

George Boorman

code-along

Machine Learning in Production with Python

Michelle Conway, Lead Data Scientist at Lloyds Banking Group, will walk you through a simple machine learning example on banking data, including feature engineering, training a model, making predictions, and assessing model performance.

Michelle Conway

See More See More