This course will teach you how to manage the end-to-end lifecycle of your machine learning models using the MLflow managed service on Databricks.

In this session, Katie will explore the potential pitfalls of ML design and how to avoid them, and will showcase the importance of a human-centric approach to building an ethical and long-lasting AI strategy.

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

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

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

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

Pre-trained models are used everywhere right now to add AI functionalities to products. But have you wondered how they are trained? This course will teach you from start to finish the process of going from an idea and a dataset to a trained model.

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Learn to streamline your machine learning workflows with tidymodels.

Unlock the power of modular RAG architectures. This course will teach you to decompose RAG models into independent modules, implement flexible and reusable designs, and evaluate their performance, scalability, and maintainability for optimal results.

Enhance your machine-learning models! This course will teach you the tools and techniques to effectively monitor and evaluate model performance during training.

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