Python GenAI & NLP

The modern AI stack, hands-on. Eighteen tutorials cover transformers, BERT and GPT, HuggingFace, LLM APIs with function calling, prompt engineering, fine-tuning with LoRA, AI agents, RAG, vector databases, evaluation, and guardrails. It all ends with a capstone where you build a complete AI application.

Choosing an AI Tech Stack: 3 Real Case Studies

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Teams pick the shiny tool first, bend the problem to fit it, and six months later a large language model is doing a job a hundred lines of scikit-learn would have nailed. Choosing an AI tech stack is where projects quietly go wrong before any model code exists. This post gives you a four-axis decision framework, then walks three real case studies to very different, defensible stacks. “The best stack is the boring one that clears your acceptance metric and ... Read More

How to Add AI to an Existing App Without Breaking It

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When you add AI to existing app code, the danger is almost never the model itself. It is everything the model touches on the way in and out: the endpoint you wired it into, the request that now hangs for ten seconds, the bill that arrives at the end of the month. “Add the new thing at the edge, where you can rip it out again, not through the middle where it fuses to everything.” Advice worth more than most ... Read More

Airflow Tutorial: Your First Data Pipeline, the Right Way

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A cron job runs your script at 2 a.m. and says nothing when it fails; you find out at 9, staring at a dashboard full of holes. This Airflow tutorial replaces that fragile setup with a real data pipeline: one that retries network hiccups, refuses to load bad data, and can rerun any day without making a mess, built end to end on real data. “A pipeline is not code that runs once. It is code that has to run ... Read More