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Part 9: Turing LLMOps — Proprietary Intelligence

You've built AI-native applications using foundation models (Parts 1-8)—prompting, orchestrating agents, deploying at scale. Now you'll learn when and how to go beyond off-the-shelf models to create proprietary intelligence through custom model training, fine-tuning, and managed deployment.

This part introduces LLMOps (Large Language Model Operations)—the discipline of training, deploying, and operating custom AI models in production using the Turing platform.

Why Proprietary Intelligence Matters​

Foundation models (GPT, Claude, Gemini) are powerful but generic. They know everything about nothing specific. When you need:

  • Domain expertise: Medical diagnosis, legal reasoning, financial analysis
  • Proprietary workflows: Your company's specific processes, terminology, decision patterns
  • Competitive advantage: Capabilities competitors can't replicate by prompting ChatGPT
  • Cost optimization: Running specialized smaller models cheaper than giant foundation models
  • Data sovereignty: Keeping sensitive data within your infrastructure

You need proprietary intelligence—models trained on your data, optimized for your tasks, deployed under your control.

What You'll Learn​

LLMOps Fundamentals​

You'll understand the complete lifecycle of custom models:

  • When to fine-tune: Identifying problems that need custom models vs. better prompting
  • Data preparation: Curating training datasets, quality control, safety validation
  • Training workflows: Managed fine-tuning without deep PyTorch expertise
  • Evaluation frameworks: Measuring model quality, safety, and task performance
  • Deployment patterns: Serving custom models through APIs, SDKs, and agent backends

Turing Platform Mastery​

You'll learn the Turing platform's managed LLMOps workflow:

  • Account setup: Environments, access control, and project organization
  • Data pipelines: Uploading datasets, cleaning, and preparing for training
  • Fine-tuning: One-click managed training with checkpoints and rollback
  • Quality gates: Automated evaluation with acceptance thresholds
  • Model deployment: Versioning, traffic splitting, and A/B testing
  • Production operations: Monitoring latency, errors, cost, and safety

Integration with Your Stack​

You'll connect custom models to systems you've built:

  • Agent backends: Using custom models with OpenAI Agents SDK, Google ADK, Anthropic Agents Kit
  • MCP servers: Serving custom models through Model Context Protocol
  • FastAPI edges: Building API gateways for custom model endpoints
  • Cloud-native deployment: Integrating with Kubernetes, Dapr, and your production infrastructure

Prerequisites​

This part builds on everything from Parts 1-8:

  • Part 4 (SDD-RI): You'll write specifications for model training—defining tasks, success criteria, evaluation frameworks
  • Part 5 (Python): Understanding data processing, evaluation scripts, and API integration
  • Part 6 (AI Products): Applying evaluation-first thinking to model quality assessment
  • Part 7 (AI Native): Integrating custom models with agent frameworks (OpenAI SDK, MCP)
  • Part 8 (Cloud Native): Deploying custom model endpoints in production infrastructure

What Makes This Different​

Most AI courses teach you to use models. This part teaches you to create models.

Traditional approaches:

  • Require deep ML expertise (PyTorch, distributed training, GPU management)
  • Focus on research problems (achieving SOTA benchmarks)
  • Assume unlimited compute budgets

LLMOps with Turing focuses on:

  • Managed workflows: One-click training without infrastructure expertise
  • Business problems: Improving task performance, reducing costs, maintaining quality
  • Production constraints: Latency budgets, cost per request, safety requirements

You're not becoming an ML researcher. You're becoming an LLM product engineer—someone who knows when custom models create value and how to deploy them reliably.

Real-World Applications​

Proprietary intelligence applies across industries:

Healthcare: Fine-tune models on medical literature + clinical guidelines for diagnosis assistance Legal: Train on case law + firm precedents for contract analysis Finance: Specialize models on market data + regulatory documents for compliance Customer Support: Customize on product docs + support tickets for accurate helpdesk automation Software Development: Train on your codebase + documentation for specialized coding assistants

The pattern: Foundation model knowledge + Your proprietary data = Competitive advantage

Part Structure​

This part progresses through four stages:

Stage 1: Concepts & Setup​

Understanding what proprietary intelligence means, when it's worth the investment, and how to set up your Turing environment. Mapping your use cases to Turing capabilities.

Stage 2: Data & Training​

Preparing training datasets with quality control and safety passes. Running managed fine-tuning workflows with checkpoints and rollback capability. Understanding the tradeoffs between model size, training cost, and inference performance.

Stage 3: Evaluation & Quality​

Implementing task-specific evaluations and safety assessments. Defining acceptance thresholds for model quality. Understanding when a model is "good enough" to deploy vs. needs more training.

Stage 4: Deployment & Operations​

Deploying model endpoints with versioning and traffic management. Integrating with your agent frameworks and API infrastructure. Monitoring production performance: latency, errors, token usage, cost. Building incident response playbooks for model rollback and safety issues.

Pedagogical Approach​

This part uses Layer 4 (Spec-Driven Integration) thinking:

  • You'll write specifications for model training tasks
  • You'll define evaluation criteria before training begins
  • You'll integrate custom models into production systems you've built in Parts 7-8
  • You'll make strategic decisions: when to fine-tune vs. improve prompts, when to use smaller models vs. foundation models

Layer 3 (Intelligence Design) also applies:

  • You'll create reusable evaluation frameworks for model quality
  • You'll design data preparation pipelines that scale across projects
  • You'll build monitoring dashboards for production model operations

Success Metrics​

You succeed when you can:

  • ✅ Identify problems where custom models create value vs. over-engineering
  • ✅ Prepare high-quality training datasets with appropriate safety controls
  • ✅ Run fine-tuning workflows and evaluate results against success criteria
  • ✅ Deploy custom models to production with proper monitoring
  • ✅ Make cost-quality tradeoffs: when to use custom models vs. foundation models
  • ✅ Integrate custom models with agent frameworks and production infrastructure

What You'll Build​

Practical projects demonstrating LLMOps mastery:

  1. Domain-Specific Assistant: Fine-tune a model on domain knowledge (medical, legal, technical) and deploy through MCP server
  2. Cost-Optimized Agent: Replace expensive foundation model calls with specialized smaller model for specific tasks
  3. Production Pipeline: End-to-end workflow from data curation → training → evaluation → deployment → monitoring

By the end, you'll have the skills to evaluate "should we fine-tune a model for this?" and execute the complete lifecycle if the answer is yes.

Looking Ahead​

After mastering LLMOps, you're ready for Part 10: TypeScript—learning the language of realtime interaction and frontend development. You'll build user interfaces that connect to your custom models, creating complete AI products from backend intelligence to frontend experience.

Turing + TypeScript enables the full stack: Train custom models in Part 9, build interactive UIs in Part 10, deploy realtime systems in Part 11.