AI and Machine Learning Development
Begin with the decision, available data and consequences of error. We scope data preparation, model development, evaluation and deployment around an explicit use case, so feasibility and limitations are understood before a demonstration becomes a production commitment.
Assess the decision and the available data
A modelling project starts with a specific decision, the input data and the consequences of error. Review the representativeness, quality, permitted use and preparation needs of the data before selecting an approach. A feasibility assessment can reveal whether a simpler rule or a model is the more useful next step.
- Use case, users and decision context
- Available data, labels and coverage
- Known edge cases and consequences of error
- Data preparation and evaluation scope
Is this the right service for your project?
Choose this service for the data and model work behind a defined decision. IoT connects data-producing devices and platforms; Edge AI addresses deployment on or near the device.
Useful starting inputs
- The user or business decision to improve
- Representative data and its permitted use
- Available labels, known edge cases and error costs
- Deployment context and acceptance criteria
Outputs to agree in the scope
- Data/feasibility assessment and modelling scope
- Evaluation plan with relevant baseline and failure cases
- Model/data-pipeline development and deployment scope
- Limitations, monitoring and change-management considerations
Evaluate behaviour beyond a demonstration
Agree how the proposed model will be assessed and what a useful result would mean in the operating context. Evaluation should make limitations and difficult cases visible, rather than relying on a small set of convincing examples. Measures and acceptance criteria are chosen for the particular task.
- Relevant baseline and evaluation approach
- Representative and difficult test cases
- Performance, infrastructure and interpretation needs
- Limitations and decisions before deployment
Connect the model to the system using its output
Deployment links the model with real inputs, software interfaces and the people or products using its output. Define the integration, monitoring and change responsibilities. When the model must run near a device, connect the work to an Edge AI and embedded-system assessment.
- Input/output interfaces and deployment environment
- Data-pipeline and operational responsibilities
- Monitoring, feedback and future model changes
- Cloud, local or embedded deployment boundary
Agree the next milestone and handoff.
The final scope defines the deliverables, review responsibilities and any third-party licence or ownership boundaries. Share the next milestone so the work can be scoped around a useful result.
Start with your project brief.
Share the current product stage, the work you need and the next milestone.
Questions about ai and ml development
Can the project begin with a feasibility assessment?
Yes. A defined use case, representative data and the decision you want to improve are enough to begin scoping an assessment. The result should clarify assumptions, preparation work and the next useful evaluation.
Do you also help prepare data?
Data preparation and pipelines can be part of the scope alongside model development. Quality, labels, coverage, permitted use and future data responsibilities should be considered before a model is treated as the complete solution.
Can you guarantee a particular accuracy?
A useful target depends on the task, data, evaluation method and consequences of error. Agree how the result will be measured and assessed; a general accuracy promise is not a substitute for that work.
Can the model be deployed on a device?
Where a device or local system is the appropriate deployment boundary, the Edge AI service assesses the model together with compute, memory, power, sensing and embedded integration requirements.
Continue your project
Make the next milestone a useful one.
Tell us what exists today, what needs to change and the decision your team needs to make. We’ll connect the discussion with the relevant engineering scope.
