ManuAI
VI

Tech track · 8 steps · 56 min

ML Engineer

“I want to build machine learning models and put them into real products”

From your first scikit-learn model to a deployed, monitored service and a working LLM feature. For people who can code a little and want the engineering side of machine learning.

3–4 hours a week for 6 weeks

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01

The steps

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  1. 01What ML engineers actually doYou can explain the job, how it differs from data science, and what you will spend your days on.
  2. 02Python and the core librariesYou can set up a free notebook, load a dataset with pandas and train a first model with scikit-learn.
  3. 03The ML workflow, end to endYou can walk a problem from a vague question to a trained model, and say where each step can go wrong.
  4. 04Evaluation done rightYou can split data properly, spot leakage, and pick a metric that matches the real cost of mistakes.
  5. 05Classic models that still winYou know when to reach for linear models, decision trees and gradient boosting, and why they beat deep learning on most tables.
  6. 06Shipping and monitoring modelsYou can wrap a model in an API, package it, and know what to monitor once real users depend on it.
  7. 07Working with LLMsYou can build a small feature on a language model API, ground it with retrieval, and measure whether it works.
  8. 08Responsible ML and your portfolio projectYou can check a model for bias and privacy risks, and you have a plan for one project that shows you can do the job.

02

Quiz and certificate

ML Engineer track quiz

Tick every step, then pass the 10-question quiz with 80% or more. The certificate comes with a link anyone can check.

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03

Where to go next

Free, reputable resources for going deeper. They open on other sites.