Category
AI Engineering
I build AI systems for a living. These posts cover what it actually takes to get models, agents, and pipelines into production — the architecture decisions, the failure modes, and the patterns that work across industries.
What You'll Find Here
- Production-focused implementation patterns for AI Engineering.
- Architecture and tooling decisions that hold up beyond prototypes.
- Evaluation and reliability practices to keep AI systems trustworthy.
- Focused tracks: Agents & Automation, LLMs in Production, Data & ML Pipelines.
Subcategories
Agents & Automation
Multi-agent systems, LangGraph workflows, tool use, and autonomous AI pipelines.
LLMs in Production
Taking language models from prototype to production — RAG, evaluation, hallucination mitigation, and deployment.
Data & ML Pipelines
Databricks, SageMaker, feature engineering, model training, and ML infrastructure.