Machine Learning Engineers

Beat a sensible baseline with methods you can explain, before reaching for anything deeper.

• Scikit-learn pipelines, cross-validation and hyperparameter search
• Feature engineering — encoding, scaling and selection
• Choosing the metric that matches the business question
• Reading a confusion matrix like an operator, not a statistician

You ship: A trained baseline model that beats a naive benchmark, with results you can defend.

Apply deep learning where it earns its cost — and learn to tell when it does not.

• Training loops and transfer learning in TensorFlow or PyTorch
• Computer vision task patterns — classification, detection, segmentation
• Natural language processing with Hugging Face models
• Error analysis: when to fix the data instead of the architecture

You ship: A trained deep-learning model with a documented error analysis.

Make every result reproducible and every model accountable.

• MLflow — runs, parameters, artifacts and the model registry
• Versioning data alongside models
• Comparing experiments honestly
• Model cards: documenting intended use and known limits

You ship: Your experiments tracked and your best model registered with a model card.

A model that nothing can call is not a system. This week it goes behind an endpoint.

• Containerising with Docker for reproducible serving
• Batch and real-time serving paths
• Monitoring drift, data quality and performance decay
• Retraining loops and rollback

You ship: Your model served behind an endpoint, with monitoring in place.

Prove the whole pipeline holds, from raw data to a monitored prediction.

• Cost, scale and failure modes end to end
• Portfolio packaging — what a hiring team needs to see
• Runbook and handover documentation
• Demo day — present to mentors and hiring partners

You ship: An end-to-end ML system with its model card and runbook.