Who this track is for
The ML Engineer track is for engineers who want to own the full lifecycle of ML systems — from training runs to production serving. You’ll go deep on PyTorch, learn how to scale training across GPUs, and build the infrastructure that keeps models healthy in production. Best fit if you:- Come from a software or data engineering background
- Want to work on ML infrastructure at companies with serious model training
- Are interested in the systems and engineering side of ML
Curriculum
Level 1 — Deep Learning Foundations (free)
| Module | Key Topics |
|---|---|
| Neural Networks & PyTorch | Tensors, autograd, training loops, GPU basics |
| CNN & RNN Architectures | Conv layers, sequence models, practical applications |
Level 2 — Training at Scale
| Module | Key Topics |
|---|---|
| Advanced PyTorch | Custom datasets, DataLoaders, mixed precision, profiling |
| Distributed Training | DDP, model parallelism, gradient accumulation |
Level 3 — MLOps
| Module | Key Topics |
|---|---|
| Experiment Tracking | MLflow, Weights & Biases, hyperparameter tuning |
| Model Registry & CI/CD | Versioning, automated retraining, model promotion |
| Data Pipelines for ML | Feature stores, data versioning, lineage |
Level 4 — Model Serving
| Module | Key Topics |
|---|---|
| Inference Optimisation | Quantisation, pruning, ONNX, TensorRT |
| Serving Systems | BentoML, Triton, latency budgets, autoscaling |
| Production Monitoring | Shadow deployment, A/B testing models, drift |