AI
9 items
Series Posts
Part 0A: Neural Networks & The Learning Mechanism
Building deep intuition from first principles — how neural networks actually learn, explained step by step for engineers who want to truly understand the machinery before architecting with it.
Part 0: Python for Java Developers: From Streams to Tensors
A 10-part, example-driven Python series for seasoned Java and C# developers moving into data science, ML and AI. Follow Arjun, a Java architect pulled into a fraud-detection ML project, from culture shock to PyTorch.
Part 0: Redis Deep Dive: From Cache to AI Memory
An extensive 8-part Redis tutorial series written for senior engineers. It follows the story of Maya, a Staff Engineer at GlobalMart, who transforms a struggling monolith into a distributed, AI-powered platform using Redis.
Part 0B: From Sequences to Transformers
The journey from “words in order” to “understanding meaning” — how we taught machines to process language, and why the Transformer changed everything.
Part 1A: Understanding the LLM Machine
The technical foundations that drive every architectural decision — transformers, embeddings, and tokenization- are explained through the lens of cost, performance, and trade-offs.
Part 1B: Making Decisions with LLMs
From model selection to production reliability — the decision frameworks that separate prototype AI from enterprise systems.
Part 2A: Production RAG: What Tutorials Don’t Teach You
From naive retrieval to production-grade systems — the architectural patterns, chunking strategies, and retrieval engineering that separate demo RAG from enterprise RAG.
Part 5: The Intelligent Layer — Azure AI & Machine Learning
CloudVault wants to add intelligent features to their platform. Marcus explores Azure AI Services (Vision, Language, Speech), Azure OpenAI for LLMs, and Azure Machine Learning. We integrate AI capabilities into a Java backend application and understand when to use pre-built APIs vs. custom models.
Part 10: "Into the Deep End" — PyTorch, Concurrency and the AI Toolkit
Tensors, autograd and a hand-written PyTorch training loop. Then the questions a Java architect asks: the GIL, threads vs processes, asyncio for fanning out LLM calls, embeddings and semantic search, validated structured output, and how Python models reach JVM services.