How to build robust testing and QA pipelines for ML systems, covering unit tests, integration tests, and evaluation frameworks.
Learn efficient strategies for fine-tuning large language models with limited computational resources, covering LoRA, QLoRA, domain adaptation, and optimal training practices.
A comprehensive guide to Retrieval-Augmented Generation systems, covering vector databases, embedding models, and how to build production-ready RAG pipelines.
A comprehensive guide to evaluating AI models, covering benchmark datasets, evaluation metrics, and frameworks for assessing model performance, fairness, and reliability.