Machine Learning
Machine learning is worth reaching for when the rule you need cannot be written down. If you can express the logic in if-statements, write the if-statements. If the answer depends on patterns across thousands of past examples, that is a model.
How we approach it
Check the data before promising anything
Most ML projects are decided here. Enough examples, labelled consistently, representative of what you will actually see. This assessment is fast and it is honest.
Baseline with something simple
A simple model or a good heuristic sets the bar. If a complicated approach cannot beat it clearly, the simple thing wins.
Deploy behind a real interface
A model in a notebook has produced no value. It ships as an API or inside the product, with monitoring.
Watch for drift
Models decay as the world moves. Retraining is planned in from the start, not discovered when accuracy falls.
Common questions
How much data do I need for machine learning?
It depends more on how varied your data is than the raw count. Narrow, well-labelled classification can work from a few thousand examples; noisy or highly varied problems need far more. We assess this before scoping, because it decides whether the project is viable.
What is the difference between machine learning and AI automation?
AI automation usually applies an existing general model to your workflow. Machine learning trains a model on your specific data for a specific prediction. Automation is faster to deploy; a trained model tends to be more accurate on a narrow task.
How do you know if a model is good enough to ship?
Against a baseline and a threshold agreed before training, on data the model has never seen. We also define what happens when it is uncertain — because a model that quietly guesses is worse than one that says it does not know.
Often paired with
Talk it through
Tell us what you're trying to do and we'll tell you honestly whether this is the right way to do it.