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MLOps & Observability

Pipelines, monitoring, drift detection, and rollout strategies.

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Latest in MLOps & Observability

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Building Cloud-Native AI Solutions at Scale

Architecting intelligent systems for performance, reliability, and global deployment. Patterns and practices for building cloud-native AI that scales from startup to enterprise.

Tom Hundley

December 10, 2025

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Engineering Agentic Reliability

4 of 4 parts

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Engineering Agentic Reliability•Part 1/4

State Management: Why Chatbots Forget (And How to Fix It)

Why do chatbots forget context? The difference between vector 'memory' and true 'state.' How to use state machines (LangGraph) to maintain variable integrity across a 50-step process.

December 10, 2025

Engineering Agentic Reliability•Part 2/4

Robust Tool Definitions: Pydantic, JSON Schema, and MCP

If your tool definition is vague, your agent will fail. Best practices for Pydantic validation, error handling, and designing 'unbreakable' tools that recover gracefully from bad LLM calls.

December 10, 2025

Engineering Agentic Reliability•Part 3/4

The 'Reviewer Pattern': Automated QA for Agent Code

Never let an agent push code to production without a review. How to build a 'Critic' agent that reviews, lints, and rejects the work of the 'Builder' agent before a human ever sees it.

December 10, 2025

Engineering Agentic Reliability•Part 4/4

Evals or Die: Unit Testing for Stochastic Systems

If you don't test it, you can't deploy it. But how do you unit test a probability engine? Strategies for 'LLM-as-a-Judge,' deterministic mocking, and continuous evaluation pipelines.

December 10, 2025

AI Engineering Foundations

4 of 4 parts

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AI Engineering Foundations•Part 1/4

Fine-Tuning LLMs for Enterprise: Cloud vs Local Guide

Fine-tuning is powerful but often misused. Learn when to fine-tune, how to do it right (cloud and local), and why prompt engineering or RAG might be better choices.

December 10, 2025

AI Engineering Foundations•Part 2/4

Model Distillation: Build Smaller, Faster, Cheaper AI

The future isn't bigger models—it's smarter small ones. Learn how to distill large models into efficient, task-specific versions for production deployment.

December 10, 2025

AI Engineering Foundations•Part 3/4

RLHF Explained: Reinforcement Learning in Production AI

RLHF made ChatGPT useful. Understanding how reinforcement learning shapes AI behavior helps you understand what AI can—and can't—become in your organization.

December 10, 2025

AI Engineering Foundations•Part 4/4

AI Model Drift Detection: Keep Your Models Honest

Models don't fail all at once—they drift. Learn to detect data drift, concept drift, and model drift before small degradations become major production failures.

December 10, 2025

Building Cloud-Native AI Solutions at Scale

Architecting intelligent systems for performance, reliability, and global deployment. Patterns and practices for building cloud-native AI that scales from startup to enterprise.

December 10, 2025

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