Understand how vector databases store and search AI embeddings efficiently. Learn similarity search, indexing, and semantic retrieval techniques. Connect vector databases with LLM applications. Build scalable search and recommendation systems. Explore modern AI infrastructure components.
Master enterprise-grade AI engineering using production-ready architectures, distributed model training, GPU acceleration, transformer networks, vector embeddings, and scalable inference pipelines. Implement TensorFlow, PyTorch, Hugging Face, CUDA, ONNX Runtime, TensorRT, and modern MLOps workflows. Design high-performance solutions with RAG, semantic search, FAISS, Pinecone, LangChain, Kubernetes, and cloud-native deployment strategies. Develop optimized, production-ready AI applications following industry best practices for scalability, observability, security, and performance.
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