Course Wiki

Module 5: Deploying Machine Learning Models — Machine Learning Zoomcamp

Lessons, materials, and key concepts for module 5 of Machine Learning Zoomcamp.

Machine Learning Zoomcamp > Module 5: Deploying Machine Learning Models

5. Deploying Machine Learning Models

Note: these materials are partly outdated, which is why we recorded a workshop that updates this module. You will find the materials in the workshop/ folder

The theory in the module is still relevant, which is why we suggest you to watch both the workshop and the module content. While watching the module content, focus on the theory part. The practical part is all covered in the workshop.

• 5.1 Intro / Session overview
• 5.2 Saving and loading the model
• 5.3 Web services: introduction to Flask
• 5.4 Serving the churn model with Flask
• 5.5 Python

Lessons

Key concepts

• Docker
• Flask
• Model Deployment
• Spec-Driven Development
• scikit-learn
• Gradient Boosting
• Decision Trees
• FastAPI

Homework and deadlines live in the course repository: machine-learning-zoomcamp.

Other modules in this course

• Module 1: Introduction to Machine Learning
• Module 2: Machine Learning for Regression
• Module 3: Machine Learning for Classification
• Module 4: Evaluation Metrics for Classification
• Module 6: Decision Trees and Ensemble Learning
• Module 8: Neural Networks and Deep Learning
• Module 9: Serverless Deep Learning
• Module 10: Kubernetes and TensorFlow Serving