Machine Learning Engineer

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

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

Perform a machine learning experiment to find the best model that predicts the temperature in London!

Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.

Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

This course covers everything you need to know to build a basic machine learning monitoring system in Python

Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control