Machine Learning
TensorFlow, PyTorch, Scikit-learn
Develop and deploy machine learning models for prediction, classification, and pattern recognition using industry-leading frameworks.
- Model development and training
- Feature engineering
- Model deployment (MLOps)
- Model monitoring
- A/B testing
- Predictive capabilities
- Automated decisions
- Pattern recognition
- Continuous learning
- Predictions
- Recommendations
- Fraud detection
- Image recognition
Common questions
What is the difference between this and the generative AI work?
Generative AI uses an existing large model and adapts it with prompting and retrieval. Classical machine learning means training a model on your data for a specific prediction - churn, demand, risk scoring, classification. Different problems, different economics: training needs labelled data and more upfront work, but the resulting model is small, cheap to run and predictable.
How much labelled data do we need?
It depends on the problem, but be sceptical of anyone answering without seeing yours. The more useful question is whether labelling is feasible at all, because it is usually the largest hidden cost in the project. We assess what you already hold - historical outcomes are often labels nobody realised they had.
What is MLOps, and do we need it?
It is the machinery that keeps a model working after it ships: reproducible training, versioned data and models, monitoring for drift, and a way to roll back. You need it the moment a model influences real decisions. Models degrade quietly as the world changes, and without monitoring the first sign is a business metric moving for reasons nobody can explain.
How do you avoid bias in the model?
By treating it as a measurable property rather than an intention. That means evaluating performance across the groups the decision affects, not just overall accuracy - a model can be 95 percent accurate overall and much worse for one segment. Where the decision affects people materially, that evaluation belongs in the acceptance criteria and in the documentation.