Archive
Structured guides and deep dives across Python, AI/ML, automation, and modern infrastructure. From first principles to production.
MLOps & Infrastructure
Bring together FastAPI, SQLAlchemy, and Pydantic to build a complete inventory management system with layered architecture, CRUD operations, and production-ready patterns.
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MLOps & Infrastructure
You've trained a killer machine learning model. It works great on a few samples, but now you need to run predictions across millions of records in your data warehouse.
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Claude Code
Ever watched your CI/CD pipeline spend half its time on repetitive tasks that could be automated away?
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MLOps & Infrastructure
You deploy a machine learning model to production, and everything works - during testing. Then real users hit it, and you're looking at 45-second response times.
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Python Fundamentals
Build production-grade REST APIs with FastAPI, leveraging automatic validation, interactive documentation, async support, and dependency injection for clean, scalable web services.
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Claude Code
If you've ever stared at a monitoring dashboard at 2 AM wondering "do we need more infrastructure?", you know capacity planning is both critical and tedious.
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Python Fundamentals
Build reliable, ethical web scrapers with Python using BeautifulSoup and requests, from basic HTML parsing to production-ready systems with pagination, error handling, and database storage.
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MLOps & Infrastructure
You've deployed a large language model to production. Requests arrive individually - sometimes singly, sometimes in bursts.
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Claude Code
You're staring at a codebase that's grown over three years. You know something feels... off. Modules are tightly coupled. Dependencies flow in circles. New features take twice as long to ship.
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MLOps & Infrastructure
You've just deployed a Llama-3-70B model to production, and your inference latency is unacceptable. A single GPU can't hold the weights, and even if it could, token generation is painfully slow.
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