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Series · Day 100 of 100

Practical AI & Machine Learning

  1. 51
    Prompt injection and AI security for AI & ML developers
    Untrusted text can hijack your model.
  2. 52
    Fine-tuning vs RAG vs prompting: lessons from production
    Pick the cheapest approach that works.
  3. 53
    Recommendation systems basics: a practical AI & ML guide
    Suggest products, articles or users.
  4. 54
    Image classification basics: what developers should know
    Teach models to recognize what is in a picture.
  5. 55
    OCR and document extraction: what developers should know
    Turn PDFs and receipts into structured data.
  6. 56
    Saving and versioning models: a practical AI & ML guide
    Treat models as build artifacts.
  7. 57
    Deploying ML models with FastAPI for AI & ML developers
    Serve predictions over HTTP.
  8. 58
    Batch vs real-time predictions: best practices explained
    Not every model needs an API.
  9. 59
    Model monitoring in production: best practices explained
    Models degrade quietly.
  10. 60
    MLOps basics: concepts, trade-offs and production lessons
    Bring software engineering discipline to ML.
  11. 61
    Experiment tracking with MLflow: lessons from production
    Remember what you tried and why.
  12. 62
    Feature stores explained: a AI & ML guide for developers
    Share features between training and serving.
  13. 63
    Explainable AI with SHAP: a AI & ML guide for developers
    Understand why a model made a prediction.
  14. 64
    Bias and fairness in ML: AI & ML lessons from production
    Models learn the biases in their data.
  15. 65
    Data privacy in AI features: what developers should know
    Respect user data while using AI.
  16. 66
    AI analytics: natural language to SQL, explained simply
    Let users ask questions about their data.
  17. 67
    AI report generation in Practical AI & Machine Learning
    Turn raw metrics into readable summaries.
  18. 68
    AI-powered search for products: best practices explained
    Search that understands intent.
  19. 69
    Speech-to-text and text-to-speech for AI & ML developers
    Voice features for applications.
  20. 70
    Computer vision use cases for apps: a step-by-step guide
    Practical vision features you can ship.
  21. 71
    Running models locally: AI & ML best practices explained
    Open models on your own hardware.
  22. 72
    Choosing an LLM for your product for AI & ML developers
    Quality, latency, cost and privacy.
  23. 73
    Synthetic data for testing ML: a practical AI & ML guide
    Generate data when real data is scarce or sensitive.
  24. 74
    Labeling data efficiently: practical tips and trade-offs
    Good labels are worth more than model tweaks.
  25. 75
    Active learning: what every AI & ML developer should know
    Label the examples that teach the model most.
  26. 76
    Math you actually need for ML: a practical AI & ML guide
    Enough to understand, not to publish.
  27. 77
    A/B testing ML models in Practical AI & Machine Learning
    Prove a model helps before full rollout.
  28. 78
    Building an AI SEO assistant: a practical AI & ML guide
    A practical AI product from start to finish.
  29. 79
    Machine learning interview questions: a practical guide
    Questions that test practical understanding.
  30. 80
    AI engineering interview questions: a step-by-step guide
    What teams ask engineers building LLM features.
  31. 81
    Common ML mistakes in production for AI & ML developers
    Lessons from models that failed.
  32. 82
    Common LLM integration mistakes: lessons from production
    Pitfalls in AI features.
  33. 83
    From notebook to production: what developers should know
    Turning experiments into reliable services.
  34. 84
    Cost-effective GPU usage: a AI & ML guide for developers
    GPUs are expensive. Use them wisely.
  35. 85
    Open-source vs hosted models: a practical AI & ML guide
    Control vs convenience.
  36. 86
    Multimodal AI features: AI & ML best practices explained
    Models that understand text, images and documents.
  37. 87
    AI product UX patterns: AI & ML best practices explained
    Design AI features users trust.
  38. 88
    Practical ML roadmap for developers: a step-by-step guide
    What to learn, in order.
  39. 89
    Naive Bayes for text in Practical AI & Machine Learning
    A fast, simple baseline for text classification.
  40. 90
    TF-IDF explained: what every AI & ML developer should know
    Classic text features that still work.
  41. 91
    Support vector machines: AI & ML lessons from production
    Strong classifiers for medium-size data.
  42. 92
    k-nearest neighbors in Practical AI & Machine Learning
    Predict from the most similar examples.
  43. 93
    Sentiment analysis for product reviews, explained simply
    Turn reviews into actionable signals.
  44. 94
    Named entity recognition: a AI & ML guide for developers
    Extract names, places and products from text.
  45. 95
    Summarization with LLMs: AI & ML lessons from production
    Condense long text reliably.
  46. 96
    Caching LLM responses in Practical AI & Machine Learning
    Cut cost and latency for repeated questions.
  47. 97
    Guardrails for AI output: a AI & ML guide for developers
    Keep AI features safe and on-topic.
  48. 98
    Tokens and context windows: patterns, pitfalls and fixes
    Why length limits matter.
  49. 99
    Temperature and sampling: a AI & ML guide for developers
    Control how creative a model is.
  50. 100
    100 days of practical AI and ML: the recap — a deep dive
    The lessons that matter most.