Machine LearningDevelopment Services

It depends far more on the question than on raw volume. A binary classification with clean labels can work on a few thousand well-labelled examples; forecasting usually wants two or three seasonal cycles of history. What matters more is whether the data actually contains the signal — plenty of datasets are large and still cannot answer the question being asked. The feasibility assessment exists to tell you that before you spend the build budget.

Data readiness first, by a wide margin. A project with labelled, accessible, consistent data is a fraction of the cost of one that needs a labelling effort and three system integrations before modelling can start. After that: how accurate it has to be before you will act on it, whether decisions need to be explainable for regulatory reasons, and whether it runs in batch overnight or has to respond in milliseconds.

A machine learning model makes a prediction — this claim is likely fraudulent, this machine will fail next month, this document is a contract. An AI agent takes actions, often using such a prediction as one input. If your problem is "what will happen", that is machine learning. If it is "go and do this", that is an agent. Many systems need both, and the prediction is usually the harder half.

If the logic can be written down and does not change often, write the rules — they are cheaper, faster and easier to debug. Machine learning earns its cost when the pattern is real but nobody can articulate it, when it shifts over time, or when there are too many variables to encode by hand. We would rather tell you a rules engine solves it than sell a model that does the same job with more moving parts.

Against a baseline captured before any modelling starts — usually your current process. A model with 85% accuracy sounds impressive until you learn the existing rule scores 83%. We hold out a test set drawn from real cases including the difficult ones, report the metric that matches the business cost of being wrong, and show the confusion matrix rather than a single headline number.

For most of what we build, yes. Tree-based models and linear models are interpretable by construction, and feature attribution methods cover the rest. Where a decision is regulated — credit, insurance, hiring — we design for explainability from the start rather than bolting it on, because a model you cannot defend to a regulator is a model you cannot deploy.

It will, and that is normal — the world moves and the training data ages. The failure is not drift, it is undetected drift. We monitor input distributions and live accuracy where ground truth arrives, alert when they move beyond a threshold you agree, and build the retraining pipeline as part of the project rather than as a later phase.

If ML is becoming core to your product and you can keep specialists busy permanently, build in-house — the maintenance is continuous and outsourcing it forever gets expensive. Work with us if you want a first system in production while you decide, or to prove the business case before you hire. We hand over the code, pipelines and documentation for exactly that reason.

Yes. Training and inference can run inside your own cloud account or on-premise, which is usually the requirement for regulated data. Where data must be shared, we work with anonymised or synthetic samples during development. This is worth settling in the first conversation, because it changes the architecture.

A feasibility assessment takes one to two weeks and tells you whether to continue. A first working model on real data typically follows within three to six weeks, with integration and monitoring after that. Anyone promising a production machine learning system in two weeks is either rebadging an API call or skipping the evaluation that tells you whether it works.

That is the normal starting position rather than a disqualifier. Part of the assessment is mapping where the data lives, how consistent it is, and what it would take to get it into a usable state. Sometimes the honest recommendation is to fix the data pipeline first and revisit modelling in a quarter — that is a cheaper answer than a model trained on unreliable inputs.

Pre-trained wherever it fits, which is most of the time. Fine-tuning an existing vision or language model on your data is faster, cheaper and usually more accurate than training from zero. Training from scratch is reserved for genuinely novel problems or where the data is unlike anything a public model has seen. Starting from scratch by default is a way to spend budget, not a mark of rigour.

You do. The training code, pipelines, model artefacts and any labelled data produced during the engagement are yours, handed over with documentation. Your data is not used to train anything shared with another client.

Usually far less than building it, but it is not zero and it is worth modelling upfront. Batch inference on a schedule is inexpensive; real-time inference at high volume on GPU is not. Retraining has a periodic cost too. We estimate all three during the assessment so the business case includes the running cost rather than only the build.