AI/ML for engineers who ship things
- AI Engineering Model capabilities, limitations, and selection
- AI Engineering Model APIs, streaming, and structured outputs
- AI Engineering Prompt engineering
- AI Engineering Advanced context engineering
- RAG Embeddings and semantic search
- RAG RAG and enterprise knowledge systems
- AI Engineering Fine-tuning decisions and managed workflows
- AI Engineering Multimodal applications
- Agentic AI Agentic AI, tools, and orchestration
- Agentic AI MCP and enterprise integrations
- Agentic AI Agent memory, state, and durable execution
- Harness Engineering Harness engineering and execution isolation
- AI Engineering Evaluation and experimentation
- AI Security Security, identity, and delegated authorization
- AI Security Responsible AI and governance
- Cloud AWS, Azure, and Google Cloud AI services
- DevOps Deployment, LLMOps, and AgentOps
- SRE Observability, reliability, performance, and cost
- System Design Enterprise architecture decisions and capstone projects
Who this is for
Open to professionals across the software industry — a shared learning path from your existing skills to AI engineering and architecture.
Software developers
Build AI-native products, APIs and applications.
Cloud & platform engineers
Extend cloud expertise into enterprise AI platforms.
DevOps engineers
Apply delivery and automation expertise to agentic systems.
Site reliability engineers
Engineer dependable, observable and scalable AI services.
QA & test automation
Develop evaluations, quality gates and agent test harnesses.
Data, security & other IT roles
Contribute data, governance and risk expertise to AI systems.
No single professional background is required — deepen the strengths you already have.
Go beyond the videos
Free content builds the foundation. When you're ready to go deeper, there's a structured path from where you are to AI software architect.
On YouTube
Subscribe →LangChain in production — what nobody tells you
War stories from running LangChain at scale in a real SRE environment. What breaks, what works, and the monitoring setup you actually need.
MLFlow + AWS: a complete MLOps pipeline from scratch
A deployable MLOps pipeline using MLFlow, S3, and EKS. Every config file included, costs broken down.
Bedrock vs OpenAI vs Ollama — real cost comparison
I ran the same workload through all three. Here are the actual numbers — latency, cost per 1K tokens, and when each makes sense.
The MLOps stack cheatsheet
The exact tools and architecture we use to run LLMs in production — Kubernetes setup, MLFlow config, LangSmith observability, cost controls. Free download, no fluff.