LLM Engineering & Generative AI · Tutorial tracks

Applied AI engineering

LLM APIs, structured outputs, retrieval, evaluation harnesses — Python-native application focus.

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Lesson outline

  1. 205
    Model Canary Releases

    Implement canary releases for models — Applied AI engineering.

  2. 206
    Handle Model Inference Errors

    Handle model inference errors gracefully — learn to catch, log, and recover from LLM API failures with Python. Practical steps and edge cases for robust AI apps.

  3. 207
    Add rate limiting to AI APIs

    Add rate limiting to AI APIs — Applied AI engineering.

  4. 208
    Use Prometheus for Model Metrics

    Learn to expose and monitor ML model metrics with Prometheus in this hands-on Applied AI engineering lesson. You'll set up a Prometheus client, define custom metrics, and query them.

  5. 209
    Grafana Dashboards

    Build dashboards with Grafana — Applied AI engineering.

  6. 210
    Optimize GPU Memory

    Optimize GPU memory for training — Applied AI engineering.

  7. 211
    Mixed Precision Training

    Use mixed precision training to speed up model training and reduce memory usage in PyTorch. Learn core concepts, step-by-step implementation, troubleshooting, and what to study next in the Applied AI engineering track.

  8. 212
    Profile Code with cProfile

    Profile code with cProfile — Applied AI engineering.

  9. 213
    Accelerate NumPy with Numba

    Learn how to use Numba to speed up NumPy operations. This tutorial covers the core concepts, step-by-step implementation, practical exercises, and troubleshooting tips for AI engineers.

  10. 214
    Parallelize Data Loading with Ray

    Learn to parallelize data loading with Ray in this hands-on tutorial. Master core concepts, step-by-step implementation, and troubleshooting—then move to the next lesson in the track.

  11. 215
    Use TensorBoard for Visualization

    Use TensorBoard for visualization in this Applied AI engineering tutorial — learn the core concept, follow a hands-on walkthrough, and troubleshoot common issues. Step 146 in the Python AI track.

  12. 216
    Gradient Clipping Debug

    Debug training with gradient clipping in this Applied AI engineering tutorial — hands-on steps, troubleshooting, and what to study next.

    Applied AI engineering — step-by-step tutorials

    What you will find here

    This track walks through applied ai engineering in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.