AI Practitioner Advanced
Build production-ready AI systems with advanced RAG architectures, cloud deployment, API development, fine-tuning, and enterprise-level evaluation.
Course Modules
End-to-End AI Project Lifecycle
Problem definition, model selection, development workflow, deployment readiness, evaluation metrics, and handling edge cases.
LLM APIs and Integration
OpenAI and Anthropic APIs, chat completions, streaming, function calling, token management, and cost optimization.
AI Chatbots & Conversational Systems
Conversation design, memory management, context windows, guardrails, scaling, and multi-channel integration.
Document Q&A Systems
Document processing, chunking strategies, embedding models, retrieval mechanisms, and answer post-processing.
RAG Deep Dive
Architecture patterns, vector database operations, reranking, hybrid search, metadata filtering, and RAG evaluation.
Recommendation Systems
Collaborative filtering, content-based filtering, hybrid systems, embedding-based recommendations, and A/B testing.
API Development with FastAPI
FastAPI fundamentals, AI REST endpoints, async processing, authentication, rate limiting, and API testing.
AWS AI Services
AWS AI/ML ecosystem, Amazon Bedrock, Lambda for inference, S3 pipelines, and cloud cost optimization.
Azure AI and Cloud
Azure AI services, OpenAI Service, Cognitive Services, ML Studio, monitoring, and cost management.
Model Deployment & DevOps
Docker for ML, model serving frameworks, scaling AI applications, production monitoring, and CI/CD for ML.
Fine-Tuning Techniques
Supervised fine-tuning, LoRA, QLoRA, RLHF, DPO, and fine-tuning best practices for production models.
LLM Evaluation Methods
Evaluation fundamentals, standard benchmarks, RAGAS for RAG, LLM-as-Judge, and custom evaluation pipelines.