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Track 3

Advanced AI Engineering

Design, train, and deploy production-grade, scalable AI systems including LLMs and generative AI.

Duration
6-7 months
Courses
7
Format
Live Cohort

🎯 Ideal For

Software Developers
Data Engineers
IT Professionals
Experienced ML Practitioners
AI Specialists

Course Curriculum

This track includes 7 comprehensive courses

Flexible Learning: You can take any course individually or complete the entire track to earn the Certified Generative AI & MLOps Engineer.

1

Advanced Python for AI Engineering

Build production-ready ML systems and APIs.

Key Topics:

  • Advanced OOP: Scalable ML systems, data pipelines
  • Library Mastery: Deep NumPy, Pandas, Scikit-learn
  • Performance: Vectorization, profiling, optimization
  • API Development: FastAPI and Flask for model serving
🚀
PROJECT / OUTCOME
High-performance data pipeline or custom ML library
2

Advanced Machine Learning

Master ensemble methods and model explainability.

Key Topics:

  • Feature Engineering: Advanced preprocessing and selection
  • Ensemble Models: Random Forests, XGBoost, LightGBM
  • Explainability (XAI): SHAP and LIME
  • Imbalanced Data: SMOTE and bias mitigation
🚀
PROJECT / OUTCOME
Kaggle-level modeling challenge
3

Deep Learning Masterclass

In-depth study of modern deep learning architectures.

Key Topics:

  • Architectures: CNNs (ResNet), LSTMs, GRUs, Transformers
  • Training: Transfer learning, fine-tuning, optimization
  • Modern Models: BERT and GPT architecture
🚀
PROJECT / OUTCOME
Complex image classifier or text generation model
4

Generative AI & LLMs

Build RAG systems, fine-tune LLMs, and create AI agents.

Key Topics:

  • Transformer Deep Dive: Self-attention mechanism
  • RAG: Retrieval-Augmented Generation pipelines
  • Fine-Tuning: LLaMA, Gemini on custom datasets
  • Ecosystem: LangChain, vector databases (Chroma, Pinecone, FAISS)
  • Multi-modal AI: Text, images, audio
  • AI Agents: LangGraph, AutoGen frameworks
🚀
PROJECT / OUTCOME
Domain-specific Q&A chatbot using RAG
5

MLOps and AI Deployment

Deploy scalable ML systems to production.

Key Topics:

  • Containerization: Docker & Kubernetes
  • CI/CD for ML: GitHub Actions, Jenkins
  • Cloud Platforms: AWS SageMaker, GCP Vertex AI, Azure ML
  • Monitoring: Logging, monitoring, drift detection
🚀
PROJECT / OUTCOME
Deploy end-to-end ML model as containerized API
6

Specialization Elective

Choose a domain specialization.

Key Topics:

  • Healthcare AI / Finance AI / Retail AI / Supply Chain AI
  • Domain-specific models and data
  • Regulatory challenges and compliance
🚀
PROJECT / OUTCOME
Advanced AI solution for chosen domain
7

Capstone: End-to-End AI Product

Build a complete production AI system.

Key Topics:

  • Full-Stack AI: Data pipeline → model → API → UI
  • Documentation: Production-quality docs and tests
  • Presentation: Demo value and technical architecture
🚀
PROJECT / OUTCOME
Fully deployed end-to-end AI product
🏆

Complete the Track, Earn Your Certification

Finish all 7 courses in this track to receive:

Certified Generative AI & MLOps Engineer

This industry-recognized certification validates your skills and demonstrates your expertise in advanced ai engineering to employers and clients.

Note: Individual courses do not include certification. Complete the full track to earn your certificate.

What You'll Get

👥

Live Cohort Sessions

Learn with peers in scheduled live sessions with real-time interaction

💻

Hands-On Projects

Build real-world applications with expert guidance and feedback

💬

After-Class Discussion

Continue learning with dedicated Q&A and peer collaboration

📝

Expert Feedback

Receive personalized feedback on all your projects and assignments

Ready to Start Learning?

Enroll in individual courses or commit to the full track for certification

Enroll →