Discover why MLOps is the critical trust layer for production AI. Learn how to manage data drift, concept drift, and model provenance in real-world ML systems.

And the people building them — one room, on the Toronto waterfront.

Introduction: Building production-grade generative artificial intelligence applications requires a structured, reproducible machine learning engineering workflow that extends far beyond simple API integrations. This comprehensive technical guide explores how to construct an end-to-end Large Language Model lifecycle pipeline—covering dataset engineering, Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning, automated quality gates, and containerized deployment using modern MLOps tools. Learning Objectives & Secrets: Objective 1 Master dataset preparation and freezing evaluation splits to ensure fair, unbiased baseline and fine-tuned model comparisons.

Curate Feeds | Make Collections | Customize Email Briefs

Curate Feeds | Make Collections | Customize Email Briefs

Build secure CI/CD pipelines with AWS CodePipeline & GitHub Actions. Deploy automated Generative AI via Amazon Bedrock & advanced SageMaker MLOps workflows.

This talk takes four reliability patterns from distributed systems and shows what they look like inside an agent architecture. How to shadow-test an agent. Why your circuit breakers need confidence thresholds. What an eval harness looks like when your system is non-deterministic. And why human oversight degrades faster than anyone admits.

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