Full course coming soon

Deploying and Adapting Open-Weight AI Models in Production

7 chapters · 33 lessons

1. 1. Model Selection and Licensing for Enterprise Use

   4 lessons

   Evaluate open-weight models against task requirements, hardware constraints, and legal restrictions before deployment.

2. 2. Building Inference Infrastructure

   5 lessons

   Set up production-ready serving environments with appropriate hardware, quantization, and runtime optimizations.

3. 3. Retrieval-Augmented Generation for Enterprise Knowledge

   5 lessons

   Integrate private data sources with open-weight models using vector databases and retrieval patterns.

4. 4. Fine-Tuning and Parameter-Efficient Adaptation

   5 lessons

   Adapt open-weight models to domain-specific behavior using LoRA, prompt tuning, and supervised fine-tuning.

5. 5. Production Evaluation and Safety Testing

   5 lessons

   Build automated test suites that measure correctness, bias, privacy leakage, and adversarial robustness.

6. 6. Security Controls and Failure Case Management

   4 lessons

   Implement access controls, input validation, and fallback strategies to manage risk in production AI systems.

7. 7. Governance Frameworks and Build-Versus-Buy Decisions

   5 lessons

   Create repeatable processes for model selection, vendor evaluation, and operational handoffs across teams.