Machine Learning in Production in Python

Overview

Elevate your machine learning skills to production level with this focused skill track. Designed for (aspiring) data scientists and machine learning engineers, this track offers a streamlined pathway to mastering the deployment and maintenance of machine learning models. Dive into the fundamentals of MLOps, including strategies for efficient model lifecycle management. Dive into MLflow to master interactively the deployment and tracking of machine learning models with one of the most popular MLOps tools, and apply the learned skills in a hands-on project. The track further enhances your ability to maintain and monitor deployed models, ensuring they continue to perform optimally amidst evolving data landscapes. You’ll also explore the practical benefits of versioning data using Data Version Control (DVC) to manage and version your machine learning projects effectively. By the end of this skill track, you'll be proficient in deploying, monitoring, and managing machine learning models, preparing you to successfully implement ML applications in any organization.

Syllabus

MLOps Concepts

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

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.

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Monitoring Machine Learning Concepts

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