ai-ml-development

Fine-tune an LLM for a specific domain using LoRA by applying parameter-efficient techniques to adapt Large Language Models via APIs, reducing computational overhead while customizing model behavior for targeted text classification and generation tasks.

Build a RAG system for document Q&A by integrating retrieval architectures with vector databases and applying prompt engineering patterns to connect external knowledge sources with LLM APIs for accurate information extraction.

Serve machine learning models using FastAPI to create production-ready endpoints and track experiments with MLflow to log parameters and metrics, implementing standard MLOps practices for reliable model deployment and monitoring.

PyTorch and TensorFlow support developing custom image classification models by providing foundational frameworks for building, training, and deploying machine learning architectures from initial prototyping through to production systems.

Vector database integration is needed for AI applications when implementing RAG architectures and prompt engineering patterns, enabling efficient retrieval of embedded data to supply contextual knowledge to Large Language Models.

This AI/ML development approach supports integration with OpenAI and Anthropic APIs, enabling LLM integration alongside model building with PyTorch and TensorFlow for comprehensive artificial intelligence and machine learning workflows.