MLOps & Engineering
Pipelines, reproducibility, serving, monitoring and shipping ML systems to production.
- 01What Is MLOps? From Notebook to Reliable Production SystemMost ML projects fail not because of the model but because of everything around it. We define MLOps, walk through the ML lifecycle, describe maturity levels, and examine the hidden technical debt of machine learning systems.
- 02Data Versioning, Validation and PipelinesModels are only as good as their data — and data changes. We cover versioning datasets, validating schemas and distributions, building reproducible data pipelines, and orchestration tools.
- 03Experiment Tracking: Never Lose a Result AgainML development involves hundreds of runs with different data, code and hyperparameters. We cover what to track, how to use MLflow for runs, metrics and artefacts, the model registry, and good experiment hygiene.
- 04Reproducibility in Machine LearningCan someone else — or you, in six months — get the same result? We examine sources of non-reproducibility, from seeds and GPUs to data and environments, and practical steps for reproducible research and production.
- 05Model Serving: Batch, Real-Time APIs and StreamingA model creates value only when its predictions reach people and systems. We compare batch, online and streaming serving, build a FastAPI prediction service with validation, and cover latency, scaling and safe rollout.
- 06Docker and Containers for Machine Learning"It works on my machine" is not a deployment strategy. We explain containers, write efficient Dockerfiles for ML training and serving, handle GPUs, and cover image size, security and orchestration basics.
- 07Model Monitoring: Detecting Data Drift and Concept DriftDeployed models degrade silently as the world changes. We define data drift, concept drift and prediction drift, measure drift with PSI and statistical tests, monitor without labels, and design alerts and retraining triggers.
- 08CI/CD for Machine Learning: Testing and Automating ML SystemsContinuous integration and delivery bring software-engineering discipline to ML. We cover the testing pyramid for ML — code, data and model tests — quality gates, continuous training, and a practical GitHub Actions workflow.
- 09Feature Stores and Training–Serving ConsistencyFeature stores manage features as shared, versioned assets available offline for training and online for serving. We explain training–serving skew, point-in-time correctness, online vs offline stores, and when a feature store is worth it.
- 10Edge AI and TinyML: Running Models on Phones and MicrocontrollersRunning models on-device brings privacy, offline operation, low latency and low cost. We cover the edge hardware spectrum, the optimisation pipeline, TensorFlow Lite, ONNX Runtime and TinyML on microcontrollers, and field-deployment lessons.
- 11GPUs and Hardware for Machine LearningUnderstanding hardware helps you train faster and cheaper. We explain why GPUs suit deep learning, the roles of memory capacity and bandwidth, precision and tensor cores, estimating requirements, and choosing between local, cloud and free resources.
- 12Data Labelling and Annotation: Building High-Quality DatasetsLabels are the foundation of supervised learning, yet labelling is often rushed. We cover annotation guidelines, workflows and tools, measuring agreement, handling label noise, model-assisted labelling, and fair treatment of annotators.
- 13A/B Testing and Online Evaluation of ML ModelsOffline metrics do not guarantee real-world impact. Online experiments measure what a model actually changes. We design randomised A/B tests for ML, compute sample sizes, avoid common pitfalls, and discuss ethics of experimenting with people.
- 14From Notebook to Production Code: Structuring Clean ML ProjectsNotebooks are great for exploration and terrible for production. We cover a clean project layout, configuration, modular code, typing and testing, logging, packaging, and a workflow for moving from exploration to maintainable software.
- 15Model Cards, Datasheets and Responsible DocumentationDocumentation is how models and datasets are understood, audited and used responsibly. We cover model cards, datasheets for datasets, system cards, what to include, and how documentation supports accountability and regulation.