Azure AI Search, Pinecone concepts, and embedding strategies
Deep dive on embeddings, vector indexes, similarity search, and choosing the right vector store.
This roadmap is designed from fresher to professional level: you start with fundamentals, move to guided implementation, and finish with production-grade workflows.
Models, dimensions, normalization.
Hands-on focus: Embeddings, OpenAI ada.
HNSW, IVF, cosine vs dot product.
Hands-on focus: HNSW, Similarity.
Azure AI Search, Qdrant, Redis vectors.
Hands-on focus: Vector DB, Azure Search.
Semantic search mini product.
Hands-on focus: Project, Search UI.
Developers and learners targeting AI & ML growth at Intermediate level with practical, mentor-guided learning.
Live Online delivery with 4 Weeks schedule, structured modules, and real project implementation support.
Hands-on portfolio work, interview-oriented practice, and an industry-aligned syllabus designed for hiring readiness.
Sandeep Pal
Senior Software Engineer & Mentor
