AI & ML / MLOps
LLM integration, RAG pipelines, prompt engineering, MLOps infrastructure, and building AI features that work in production.
AI Automation Foundations: 10-Part Series
A beginner to intermediate roadmap for learning AI automation, from triggers and prompts to APIs, tools, approvals, knowledge bases, monitoring, cost control, and production-ready agents.
What Is AI Automation?
A simple beginner explanation of AI automation: what it is, where it helps, how triggers, data, AI reasoning, decisions, and actions fit together.
Prompts, Instructions, and Context
Learn how to write clear AI instructions, provide useful context, define output expectations, and reduce confusing or inconsistent automation results.
Inputs, Outputs, and Structured Data
Understand why structured inputs and JSON outputs make AI automation easier to connect, validate, debug, and reuse across workflow steps.
Triggers, APIs, and Webhooks
Learn how AI automations start, receive events, fetch data, and update other systems using triggers, API calls, polling, and webhooks.
Build Your First AI Workflow
Plan and build a practical first AI workflow by choosing a trigger, collecting data, asking the model, checking the result, and taking action.
Tools, Connectors, and Permissions
Understand how AI automations use tools and app connectors, and how to keep permissions narrow, auditable, and safe.
Human Review and Approvals
Learn where to add human approval steps so AI automations can move quickly without silently taking risky business actions.
Memory, Knowledge Bases, and RAG
Learn when AI automation needs memory or retrieval, how knowledge bases work, and why RAG is useful for company-specific facts.
Error Handling, Monitoring, and Cost Control
Move from fragile demos to reliable AI automations with validation, retries, logs, alerts, metrics, evaluation, and cost limits.
Production-Ready AI Agents
Understand intermediate AI agent design: goals, planning, tool use, memory, guardrails, approvals, evaluation, and production control loops.
RAG in Production: What the Demo Does Not Show You
Retrieval augmented generation is easy to demo and hard to run reliably. The real work is in chunking strategy, picking the right embedding model, choosing a vector store, reranking results, and measuring whether any of it works.
Running ML Pipelines on Kubernetes from Training to Serving
A machine learning model is only useful if you can retrain it, version it, and serve it reliably. This post covers building that whole pipeline with Kubeflow, Argo Workflows, and MLflow on Kubernetes, right through to serving with KServe.
LLM Integration Patterns That Actually Work in Production
Adding an LLM to a product is straightforward. Making it fast, cheap, and reliable is not. This post covers prompt caching, streaming, structured output, tool calling, fallback chains, and keeping costs from getting out of hand.
Building a Conversational AI Backend That Scales
A chat interface is just the front. Behind it you need session management, context window handling, memory that works across turns, safety checks, and streaming delivery that does not keep users waiting. This post goes through all of it.