The Real-World Machine Learning Tutorial

What's inside?
01 From business to ML
Understand the business problem ?
Learn how to translate a business problem into a Machine Learning problem.
Understand the 4 steps to go from raw data to a fully working ML solution, and not just a prototype.
02 Data preparation
Transform raw data into ML data
Learn the steps to go from raw data to (features and target) data for supervised ML.
Build a robust data pipeline with Python, to fetch, validate, transform, and generate training data.
03 Model prototyping
Experimentation and model training
Learn to create quick and powerful model baselines.
Iteratively build better ML models with feature engineering and boosting trees.
Optimize model hyper-parameters to squeeze all the juice from the data.
04 Model operationalization
Deployment and Monitoring using MLOps best practices
Transform your ML model prototype into a fully working real-world batch-scoring system using a Feature Store and GitHub actions.
Build this frontend dashboard to show live predictions
Build this monitoring system
Who is it for?
You've trained an ML model before ?
If you know how to prepare data and train a model in a notebook, but don't know how to turn it into a complete ML service, this tutorial is for you.
You want to go one step further ?
If you want to learn how to design, implement and deploy a Machine Learning solution, from A to Z, this tutorial is for you.
You want to Land an ML Engineer Job ?
If you wanna master the 4 steps, that transform any business problem into an ML solution, this tutorial is for you.
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