Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
End-to-End Machine Learning
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Developing Machine Learning Models for Production
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Introduction to Shell
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Predictive Modeling for Agriculture
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
MLOps Deployment and Life Cycling
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
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.
Fully Automated MLOps
Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.
Predicting Temperature in London
Perform a machine learning experiment to find the best model that predicts the temperature in London!
Introduction to Data Pipelines
This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.
Machine Learning Monitoring Concepts
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Monitoring Machine Learning in Python
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Introduction to Docker
Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.