AI and Machine Learning

Monitor ML Models in Production (Drift)

Learn how to monitor ML models in production (drift) using statistical tests, automated pipelines, and observability tools to main...

Model versioning and registries enable reproducible ML deployments by tracking artifacts, metadata, and lineage for safe rollbacks...

Master MLOps: From Notebook to Production with a practical guide covering CI/CD, containerization, monitoring, and compliance for...

Decide whether to rent vs buy GPUs for AI workloads with a practical cost, compliance, and performance framework built for product...

Learn how to serve LLMs in production: throughput and latency optimization using vLLM, quantization, and GPU scheduling for reliab...

Learn how to build an embeddings pipeline for RAG and semantic search with production-grade chunking, vector storage, and evaluati...