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Prompt course · 8 lessons · 26 prompts · 1 hour · Beginner

AI for Machine Learning Engineers

This prompt course walks through the machine learning pipeline one lesson at a time. You get prompts for data prep, training, evaluation, deployment, monitoring, and explaining your work.

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What you'll learn

  • Data cleaning: Turn raw tables into a clean dataset with clear choices about missing values and outliers.
  • Feature splits: Create useful features and reproducible train, validation, and test splits without leakage.
  • Model design: Choose a model family, loss, and output shape, then turn a paper idea into runnable code.
  • Training runs: Diagnose training failures, read loss curves, and plan a sensible hyperparameter search.
  • Error analysis: Pick the right metrics and inspect where your model fails on real examples.
  • Serving models: Package a model into an API, container, and optimized format for production.
  • Drift monitoring: Detect drift, log live behavior, and reduce inference latency over time.
  • Clear communication: Document and explain models clearly, and absorb new research faster.

What's inside

8 lessons · 26 prompts
  1. Before you start · framework course Context Engineering and Structured PromptsContext engineering helps you turn messy ML debugging notes into structured prompts so an agent reproduces failing model training runs.
  2. Start here Priya's Thursday, two waysA day in the life of a Machine Learning Engineer, before and after these prompts.
  3. 01 Lesson 1 · 3 prompts Data Preprocessing & Cleaning
  4. 02 Lesson 2 · 5 prompts Feature Engineering & Splits
  5. 03 Lesson 3 · 3 prompts Model Architecture & Design
  6. 04 Lesson 4 · 3 prompts Training & Debugging Runs
  7. 05 Lesson 5 · 3 prompts Evaluation & Error Analysis
  8. 06 Lesson 6 · 3 prompts Deployment & Serving
  9. 07 Lesson 7 · 3 prompts Monitoring, Drift & Optimization
  10. 08 Lesson 8 · 3 prompts Communication, Docs & Learning

About this course

7 topics

Prompts for the machine learning pipeline

Machine learning work is full of small decisions that shape the final model. A good prompt can give you a starting point, a second opinion, or a checklist you would otherwise write from memory.

This course gives you prompts for each stage of the job. You stay in control. The AI assistant helps you draft code, spot gaps, and move from idea to working system with less friction.

  1. The lessons
    1. Data Preprocessing & Cleaning: Get working code and clear decisions for turning raw, messy data into a clean training-ready dataset.
    2. Feature Engineering & Splits: Turn raw columns into useful features and create leak-free, reproducible dataset splits.
    3. Model Architecture & Design: Choose the right model family, loss, and output head, and turn a paper's idea into runnable code.
    4. Training & Debugging Runs: Diagnose training failures, read loss curves, and plan a sensible hyperparameter search.
    5. Evaluation & Error Analysis: Pick the right metrics, write evaluation code, and systematically inspect where your model fails.
    6. Deployment & Serving: Package your model into a serving API, container, and optimized format ready for production.
    7. Monitoring, Drift & Optimization: Keep a live model healthy by detecting drift, logging behavior, and cutting inference latency.
    8. Communication, Docs & Learning: Document and explain your models clearly, and quickly absorb and reproduce new research.
  2. What the course covers

    The lessons follow the machine learning lifecycle. You start with data cleaning and feature engineering, then move through model design, training, evaluation, deployment, monitoring, and communication.

    Each lesson gives you a prompt pattern, examples of what to ask, and warnings about what to check yourself.

  3. How the lessons connect

    The order matters. A clean dataset makes feature work honest. Good features make training easier to debug. Careful evaluation tells you whether deployment is safe.

    You can jump to a lesson when a real problem appears, but the full sequence builds a habit of checking each stage before moving on.

  4. How to use prompts well

    Be specific about your data, model, and goal. Include the shape of your table, the metric you care about, and the constraint you cannot break.

    Ask for reasoning and alternatives, not just code. Then test the output on a small case before you trust it on the full pipeline.

  5. Who this course is for

    This course is for machine learning engineers who already write code and want to work faster with AI assistants. It also fits data scientists moving models toward production.

    You do not need to be a prompt expert. You need a real project or a realistic example to practice on.

  6. Safety and privacy for this job

    Never paste private data, credentials, or unreleased model details into a public AI tool. Use approved tools and follow your company rules.

    Check every generated line for security, bias, and correctness. You are still the engineer responsible for what ships.

  7. Your next step

    Pick one lesson that matches your current task. Run the prompt, adapt it, and note what worked. Then move to the next lesson when you are ready.

    After the course, choose a video course for deeper practice and use its certification as a milestone in your learning path.