Artificial Intelligence
AI & Machine Learning
From what a model actually is, to training and deploying your own.
AI & Machine Learning — Professional
MLOps and applied research: pipelines, monitoring, drift, cost control and responsible deployment.
130 study hours
820 pages
13 modules
PDF ebook
What is inside
01
ML system design and the full production lifecycle
02
Feature stores and reproducible data pipelines
03
Experiment tracking with MLflow and Weights & Biases
04
Model versioning, registries and rollback
05
Distributed training and GPU economics
06
Model compression: quantisation, pruning, distillation
07
Serving at scale — batching, caching, autoscaling
08
Monitoring for data drift and model decay
09
A/B testing and online evaluation
10
Explainability: SHAP, LIME and model cards
11
Bias, fairness and responsible AI in practice
12
Regulatory context and AI governance
13
Cost engineering for AI workloads
By the end you can
- Design and run a production ML platform
- Detect and correct model degradation before users do
- Lead responsible AI practice inside an organisation