Machine Learning Development

It is building systems that learn patterns from data to make predictions or decisions, such as forecasting demand, spotting fraud or recommending products. It covers data preparation, model building, deployment and monitoring.

We check this in the first few weeks. Some problems need years of history, others work with a few thousand examples, and modern pre-trained models reduce data needs for language and images. We tell you honestly if the data is not ready.

Machine learning is the broader field. Predictive models forecast, classify or detect from your data. Generative AI creates text, images or code. Many solutions combine both, and we recommend the simpler one when it does the job.

A proof of concept often takes four to eight weeks. Moving a model into production, with pipelines, testing and monitoring, typically adds two to four months depending on integration needs.

It depends on the problem and the data. We measure accuracy on data the model has not seen, compare it with your current method and report results openly, rather than promising a number before testing.

MLOps is the practice of deploying, monitoring and updating models reliably. Without it, models quietly degrade as data changes. It includes versioning, automated tests, drift monitoring and retraining.

Often, yes. We use explainable methods or add explanation tools, which matters most in areas such as lending, hiring or healthcare. We tell you where there is a trade-off between accuracy and explainability.

In your cloud account, on our managed setup, on the edge or inside your own data centre, depending on speed, cost and data rules.

We test accuracy across relevant groups, review the data for gaps and bias, and agree thresholds and review steps for decisions that affect people.

Yes. We monitor accuracy and drift, retrain on new data and report on business impact under a support plan with agreed response times.