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Machine Learning Engineer

understanding how a Machine Learning Engineer industrializes and maintains models in production

Discover the Machine Learning Engineer role: a model's life cycle, industrialization, pipelines, MLOps, collaboration with Data Scientists and Data Engineers, skills to develop, and a real-world model deployment case.

A discovery and orientation course — does not constitute a professional certification.

What you'll cover — 8 lessons

01 Discovering the career
02 Understanding day-to-day responsibilities
03 Understanding the working environment
04 Discovering the tools and technologies
05 Identifying the required skills
06 Following a concrete professional case
07 Building your path toward this career
08 Orientation workshop

Machine Learning Engineer

9,90 €

excl. VAT · 80 credits included

Your Skills Development Pathway

Recommended Mandragore courses to develop the skills required for this career.

🎯 Essential

Python

The core language for building and training ML models.

SQL

Essential for extracting and preparing training data.

Machine Learning

The core technical path for the Machine Learning Engineer role.

🧩 Recommended

ChatGPT

Speeds up model experimentation and documentation.

Power BI

Useful for presenting model performance to business teams.

Cloud Fundamentals

Model training and deployment mostly happen in the Cloud.

Data Modeling & Pipelines

Structures the data pipelines that feed models in production.

➕ Complementary

Excel

Handy for quickly exploring small datasets.