TechPlained

#Python

4 articles

AI Observability: How to Monitor and Debug LLM Applications
AI/ML Engineering

AI Observability: How to Monitor and Debug LLM Applications

A practical guide to monitoring LLM applications in production -- input/output logging, cost tracking, quality metrics, and a comparison of LangSmith, Langfuse, and Arize.

10 min read·
Deploying ML Models in Production: From Notebook to Kubernetes
AI/ML Engineering

Deploying ML Models in Production: From Notebook to Kubernetes

End-to-end guide to deploying ML models -- from ONNX export and FastAPI serving to Kubernetes GPU workloads, canary deployments, and Prometheus monitoring.

9 min read·
Fine-Tuning vs Prompt Engineering: Choosing the Right Approach
AI/ML Engineering

Fine-Tuning vs Prompt Engineering: Choosing the Right Approach

A practical guide to choosing between prompt engineering and fine-tuning for LLMs -- techniques, costs, LoRA/QLoRA, and a decision framework for production systems.

10 min read·
RAG Explained: Building AI Applications That Know Your Data
AI/ML Engineering

RAG Explained: Building AI Applications That Know Your Data

A practical guide to building Retrieval-Augmented Generation pipelines -- from document chunking and embedding to hybrid retrieval and evaluation metrics.

9 min read·

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