Machine Learning in Production

A course about what happens after training

This intermediate course from DeepLearning.AI, delivered through Coursera in English, starts from a question many machine learning courses leave open: once a model works in a notebook, what does it take to run it for real users? The answer covers far more than the model itself.

Who should take it

The course suits people who already know the basics of building models and now need to design the system around them. Data scientists moving toward engineering roles and developers asked to put a model into an existing product will find the framing practical.

What to take away

• Deployment and evaluation are treated as one connected job rather than two separate handoffs.
• Operational thinking, such as keeping a model useful over time, runs through the material.
• The skills apply across tools, so the ideas outlast any single cloud platform.

Read it as a bridge between a promising prototype and a system other people can rely on.