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Coursera Machine Learning Engineering for Production Specialization Course

License: MIT License

Jupyter Notebook 99.33% Python 0.62% Dockerfile 0.01% PowerShell 0.01% Shell 0.04%
automl data-modeling deep-learning feature-engineering kubeflow kubernetes machine-learning ml-production mlops pruning quantization tensorflow tensorflow-extended tfdv

coursera-mlops-specialization's Introduction

Machine Learning Engineering for Production

Become a Machine Learning expert. Put your machine learning knowledge to work, and expand your production engineering capabilities and begin to turn your ideas into realities.

My Scores

Course Structure

Each course is spread out in weeks, and are made up of video slides, lab sessions, quizzes, assignments, related course materials, code and data

Course 1: Introduction to Machine Learning in Production

Week 1

Slides

Labs

Week 2

Slides

Labs

Week 3

Slides

Labs

Course 2: Machine Learning Data Life Cycle in Production

Week 1

Slides

Labs

Assignments

Week 2

Slides

Labs

Week 3

Slides

Labs

Assignments

Week 4

Slides

Labs

Course 3: Machine Learning Modelling Pipelines in Production

Week 1

Slides

Labs

Week 2

Slides

Labs

Week 3

Slides

Labs

Week 4

Slides

Labs

Week 5

Slides

Labs

Course 4: Welcome to Deploying Machine Learning Models in Production

Week 1

Slides

Labs

Week 2

Slides

Labs

Week 3

Slides

Labs

Week 4

Slides

Disclaimer

The solutions presented are intended to serve as reference for other learners who enroll in this course.

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