Applied AI & ML Engineering
Applied AI & Machine Learning Engineering
Go from data to deployed models: machine learning, deep learning, MLOps and GenAI.
- Duration
- 12 or 24 weeks
- Classes
- Live online
- Learner rating
- 4.8/5
- Format
- 30% concepts + 70% hands-on
Our learners work at
Who is this for?
- College students in their 1st, 2nd, 3rd or 4th year
- Recent graduates starting a career in tech
- Anyone moving into tech from another career
- Working professionals who want to add AI, data or full stack skills to their current role
- Developers who want to move from using AI to building it
What will you learn?
- Python
- PyTorch
- Scikit-Learn
- FastAPI
- Docker
- DVC
- MLflow
- Airflow
- Vector DBs
Roles you can aim for
- AI/ML Engineer
- MLOps Engineer
- GenAI Engineer
What makes this program different
- Code-First, Zero-Math Proofs Approach
- Comprehensive Foundation (Months 1–3) Leading to Solid Core Engineering
- Advanced Industry Topics (Months 4–6): MLOps, GenAI, Vector Databases, vLLM & Agentic Workflows
- Production Model Serving & Enterprise Scaling (FastAPI, Docker, Vector DBs, Cloud Architecture)
- Dual Capstone Milestones: Mid-Course Core ML Project + Final Enterprise GenAI Capstone
Others teach linear algebra and calculus proofs. We teach Applied Machine Learning + Production Systems + Enterprise MLOps & GenAI.
Curriculum and projects
Choose your track
| Month | Focus | Outcome |
|---|---|---|
| Month 1 | Applied Machine Learning & Data Engineering | Build end-to-end ML models & data pipelines |
| Month 2 | Deep Learning, Computer Vision & NLP Systems | Build computer vision, NLP & deep learning pipelines |
| Month 3 | MLOps, Model Serving & Production Capstone | Deploy & monitor production-grade ML systems |
Month 1
- Focus
- Applied Machine Learning & Data Engineering
- Outcome
- Build end-to-end ML models & data pipelines
Month 2
- Focus
- Deep Learning, Computer Vision & NLP Systems
- Outcome
- Build computer vision, NLP & deep learning pipelines
Month 3
- Focus
- MLOps, Model Serving & Production Capstone
- Outcome
- Deploy & monitor production-grade ML systems
| Month | Focus | Outcome |
|---|---|---|
| Month 1 | Python, Data Engineering & EDA Foundations | Build clean data ingestion & advanced feature engineering workflows |
| Month 2 | Applied Machine Learning & Evaluation | Build, validate & tune supervised/unsupervised Scikit-Learn pipelines |
| Month 3 | Deep Learning Foundations & Mid-Course Capstone | Build PyTorch neural networks, CNNs & execute Mid-Course Capstone Project |
| Month 4 | Production MLOps, Model Serving & Data Versioning | Containerize models, deploy FastAPI microservices & implement data versioning (DVC) |
| Month 5 | Cloud Deployment, Scalable Pipelines & Monitoring | Deploy to cloud (AWS/GCP), orchestrate workflows & monitor data/concept drift |
| Month 6 | Industry-Grade GenAI, Vector Databases & Final Capstone | Build RAG apps, fine-tune LLMs, integrate vector DBs & execute Final Industry Capstone |
Month 1
- Focus
- Python, Data Engineering & EDA Foundations
- Outcome
- Build clean data ingestion & advanced feature engineering workflows
Month 2
- Focus
- Applied Machine Learning & Evaluation
- Outcome
- Build, validate & tune supervised/unsupervised Scikit-Learn pipelines
Month 3
- Focus
- Deep Learning Foundations & Mid-Course Capstone
- Outcome
- Build PyTorch neural networks, CNNs & execute Mid-Course Capstone Project
Month 4
- Focus
- Production MLOps, Model Serving & Data Versioning
- Outcome
- Containerize models, deploy FastAPI microservices & implement data versioning (DVC)
Month 5
- Focus
- Cloud Deployment, Scalable Pipelines & Monitoring
- Outcome
- Deploy to cloud (AWS/GCP), orchestrate workflows & monitor data/concept drift
Month 6
- Focus
- Industry-Grade GenAI, Vector Databases & Final Capstone
- Outcome
- Build RAG apps, fine-tune LLMs, integrate vector DBs & execute Final Industry Capstone
Projects and capstones
Month 3 Projects (Intermediate Milestone)
- End-to-End Customer Churn Prediction & Experiment Tracking Pipeline
- Real-Time Sentiment Analysis Web App with PyTorch & FastAPI
- Automated Real-Time Object Detection Pipeline for Inventory/Retail Tracking
Month 6 Projects (Advanced Industry-Perspective Milestone)
- Production-Grade LLM RAG (Retrieval-Augmented Generation) System with Guardrails & Observability
- Scalable MLOps Pipeline with Airflow/Prefect, Feast Feature Store & Evidently AI Monitoring
- High-Performance Edge AI Inference Engine with ONNX & TensorRT (Quantized Deployment)
Fees and enrollment
12 Weeks Foundations
12 weeks (3 months)
- Program fee
- ₹50,000
- Early bird offer
- – ₹15,000
- You pay
- ₹35,000
24 Weeks Advanced
24 weeks (6 months)
- Program fee
- ₹70,000
- Early bird offer
- – ₹21,000
- You pay
- ₹49,000
Advanced 3 months
Already finished 12 Weeks Foundations? Continue with the advanced half of the program.
- Program fee
- ₹50,000
- Early bird offer
- – ₹15,000
- You pay
- ₹35,000
Save ₹21,000 with 24 Weeks Advanced
Foundations and Advanced bought separately cost ₹70,000. The full 24 weeks costs ₹49,000, so you save ₹21,000.
Common questions
How much do the programs cost?
Each program page shows the Program fee, the Early bird offer and the amount You pay.
What payment options are available?
You can pay through UPI, and EMI options are also available. After you click Enroll now, our team shares the payment details with you on WhatsApp.
How do I enroll?
Choose your program and track, then click Enroll now. Share your name, phone number and email, and continue on WhatsApp, where our team shares the payment details and the next steps.
What is your refund policy?
If you cancel within 7 days of your cohort's start date, you receive a pro-rata refund. Please read our Refund & Cancellation Policy for the full details.
Can I move to a later batch?
Yes. You can transfer to a later batch once.
See a class before you decide
Join the free demo for this program on Saturday at 7:00 PM (IST).
From ₹35,000
View fees