Kone AI TrackNeural Architectures & Vector Search
Train custom Machine Learning models, generate vector embeddings, and build RAG AI search engines using PyTorch and FastAPI.
DIFFICULTY Advanced
DURATION 14 Weeks
PRACTICAL BUILDS 4 Micro + 2 Mini + 1 Capstone
CREDENTIAL Proficiency Certificate
Core Technologies & Stack
PythonPyTorchFastAPIPinecone/QdrantRAG PipelinesTransformers
4 Module Micro-Projects
01
Micro 1: Text Tokenization Pipeline
Preprocess raw text datasets into cleaned tensor arrays.
02
Micro 2: High-Dimensional Embeddings
Generate 1536-dimension vector embeddings using Transformer models.
03
Micro 3: Cosine Similarity Search
Query Qdrant/Pinecone vector databases with top-K rank filtering.
04
Micro 4: Streaming LLM Callbacks
Implement SSE (Server-Sent Events) in FastAPI for real-time text streaming.
2 Full Integration Mini-Projects
M1
Mini Project 1: Knowledge Ingestion & Vector Service
Build a document scraper and vector indexing API.
M2
Mini Project 2: Contextual AI Assistant UI
Build a modern chat dashboard with markdown rendering and source citations.
Production Capstone Product
Kone AI Copilot & Knowledge Search Engine
Production RAG assistant capable of answering complex documentation queries in under 500ms.
Stack: Python, FastAPI, PyTorch, Qdrant, React, Tailwind