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Advanced Track

Welcome to the Advanced Track! This 14-week program covers cutting-edge AI technologies including transformers, LLMs, and production deployment.

What You'll Learn

This track focuses on state-of-the-art AI systems and production workflows:

Week 1-4: Transformers & LLMs

  • Attention mechanisms and self-attention
  • Transformer architecture deep dive
  • BERT, GPT, and T5 models
  • Fine-tuning large language models
  • Prompt engineering and few-shot learning
  • Working with Hugging Face ecosystem

Week 5-7: Generative AI

  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Diffusion models (Stable Diffusion)
  • Image generation and manipulation
  • Text-to-image models
  • Audio generation

Week 8-10: MLOps & Production

  • Model versioning and experiment tracking
  • Docker containerization
  • Model serving with FastAPI/Flask
  • Cloud deployment (AWS/GCP/Azure)
  • Monitoring and logging
  • CI/CD for ML pipelines

Week 11-14: Capstone Project

  • End-to-end ML project
  • From research to deployment
  • Documentation and presentation
  • Portfolio showcase

Prerequisites

  • Completion of Core Track or equivalent experience
  • Strong Python and deep learning fundamentals
  • Experience with PyTorch or TensorFlow
  • Basic understanding of cloud platforms
  • Git and version control knowledge

Course Format

Each module includes: - 🔬 Research paper walkthroughs - 💡 Implementation of SOTA techniques - 🏭 Production-grade code examples - ☁️ Cloud deployment tutorials - 🎨 Creative AI applications - 📦 Complete MLOps workflows

Get Started

Open In Colab

Coming Soon: Detailed lesson modules are being developed. Check back soon or watch the repository for updates!

Capstone Project Ideas

Choose from these production-ready projects:

  • Custom Chatbot: Build and deploy a domain-specific conversational AI
  • Image Generation App: Create a web app for AI image generation
  • Document QA System: Build a question-answering system for documents
  • Real-time Object Detection: Deploy object detection on edge devices
  • Recommendation Engine: Build and scale a production recommendation system

Tools & Technologies

You'll gain hands-on experience with:

  • Frameworks: PyTorch, TensorFlow, Hugging Face Transformers
  • MLOps: MLflow, Weights & Biases, DVC
  • Deployment: Docker, Kubernetes, FastAPI, Streamlit
  • Cloud: AWS SageMaker, GCP Vertex AI, Azure ML
  • Monitoring: Prometheus, Grafana, ELK Stack

Learning Path

graph LR
    A[Foundation Track] --> B[Core Track]
    B --> C[Advanced Track]
    C --> D[Industry Ready!]
    style C fill:#7c4dff,color:#fff

After completing this track, you'll have production-ready AI skills and a portfolio of deployed projects.

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