Part 7: Turing LLMOps — Proprietary Intelligence
Parts 1-7 taught you to build, deploy, and operate AI agents with foundation models. Part 8 teaches when to go further—creating proprietary intelligence through managed fine-tuning, evaluation, and deployment on the Turing platform. You shift from consuming models to producing differentiated ones.
Goals
By completing Part 8, you will:
- Decide when to fine-tune vs. improve prompting or model selection
- Prepare high-quality datasets with quality and safety gates
- Run managed training workflows with checkpoints and rollback
- Evaluate rigorously using task-specific metrics and acceptance thresholds
- Deploy production endpoints with versioning, traffic controls, and monitoring
- Integrate custom models with your agent stack (MCP, SDKs, FastAPI, Kubernetes)
Chapter Progression
Four stages guide the LLMOps lifecycle end-to-end:
- Concepts & Setup (61-62): LLMOps fundamentals, economics, decision frameworks, and Turing platform onboarding.
- Data & Training (63-66): Data pipelines, supervised fine-tuning, persona tuning, and agentic function calling.
- Optimization & Safety (67-69): Model merging, performance tuning, alignment practices, and evaluation quality gates.
- Deployment & Integration (70-72): Serving custom models, integrating with agent frameworks, and capstone end-to-end LLMOps delivery.
Why this order? Strategic decisions come first, then data quality, then optimization and safety, and finally production deployment with integrations back into your existing systems.
Outcome & Method
You finish Part 8 able to decide whether custom models are worth the investment and, when they are, execute the full lifecycle from data to deployment. The same spec-driven approach continues: write training/evaluation specs, let AI draft pipelines, and verify against objective success metrics.