03 · AI systems
Machine learning models
Use historical data to support forecasts, scoring and classification.
A model is useful when its output improves a real decision. That may mean planning stock, reviewing unusual events or prioritising work. We help define the decision, assess the available data and compare a model with a simpler baseline before deciding how it should be used in a working system.
Ask what the prediction will change
We agree what is being predicted, how far ahead the answer is needed and what action follows it. We also discuss the cost of different errors: missing an event can matter more than raising an unnecessary review, or the reverse.
The data review looks at relevant history, missing information and whether the records reflect the situation in which the model will be used. More rows alone do not establish that a prediction will be useful.
Compare with an understandable baseline
We start with a simple reference approach and test alternatives against representative data. The comparison should reflect how the model will operate, rather than allowing information into the test that would not be available at prediction time.
We discuss performance and limitations in terms your team can use. A single accuracy figure can hide poor results on the cases that matter most, so the evaluation needs to consider those cases separately.
Plan for a model that will need attention
The delivery may be a scheduled batch process or a service connected to an application. We document the required inputs, expected outputs and the behaviour when information is missing or invalid.
We also agree how performance will be monitored, when the model should be reviewed and how a previous version can be restored. The business process remains responsible for interpreting and acting on the output.
What you receive
- Data assessment and agreed prediction task
- Baseline comparison and model evaluation
- Integration for batch or application use
- Monitoring, review and rollback guidance
A good fit when
- Historical patterns could support a recurring decision
- A manual scoring or forecasting process needs review
- A prototype model needs a defined path into daily use
Common questions
How much historical data is needed?
There is no useful universal number. It depends on the task, the variation in the data and the quality of the outcome records. We assess what is available and explain where a meaningful evaluation is not yet possible.
What if a model does not improve on the baseline?
That is a valid result of the assessment. We explain what the comparison shows and whether better data, a different task or the simpler baseline is the sensible next step.
Can you work with a model we already developed?
Yes. We can review its data assumptions, evaluation and integration needs. Taking it into production also requires a plan for operating and reviewing it, not just exposing the existing code through an API.
Contact
Start a conversation.
Tell us what you are working on and where you need help. You do not need a finished specification to start the conversation.
- Emailinfo@tekniikkatie.fi
- Phone+358 44 989 9939