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04 / AI

Machine learning & inference

Measurable intelligence, in the real world

Models for classification, estimation and anomaly detection using relevant data, with metrics and constraints defined before training.

Platforms and tools follow your context. Some examples:

Python / NumPy / C++ / TinyML / Computer vision / Edge AI

AI

Machine learning & inference

Tailored AI: goals, data, models and integration

  • Goals & use cases

    Tailored AI solutionsReal data, workflows and constraints

  • Data & preparation

    Collection · checks · transformationsData that fits the use case

  • Models & training

    Baselines · comparison · parametersResults-driven choices

  • Evaluation

    Metrics · errors · difficult casesSeparate verification data

  • Inference & integration

    Applications · services · devicesUse in the real workflow

  • Operational verification

    Latency · resources · behaviourDocumented limits and results

The technologies shown are examples.

Engineering support within your team, with shared tools and working methods.

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TECHNICAL SCOPE

Applied machine learning and inference integration

Work starts with a measurable question and a simple baseline. Data quality, representativeness and availability determine feasibility more than the model name. Training and evaluation remain separate, and inference integration considers errors, uncertainty and opportunities for human review.

Activities and verification

  • Define the problem, labels and a comparable baseline.
  • Compare models on separate data and analyse consequential errors.
  • Integrate inference into applications, services or devices according to context.

Initial decisions and integration

I define the decision the model should support and the differing consequences of errors with process specialists. I can contribute to data preparation, evaluation or integration within existing pipelines, keeping configurations and versions traceable.

A verification example

The baseline and candidate models are compared on validation data separate from training. A test set kept out of model selection supports the final evaluation. Classes, critical cases and new conditions are examined alongside the overall metric, with sample counts and test limits documented.

Deliverables and acceptance criteria

Repeatable pipeline, selected model, configuration and evaluation report. Metrics include data limits and conditions: a laboratory result is not presented as universal performance.

Where to start

Provide representative examples, success criteria, error costs and the environment where results will be used.

Can you evaluate an existing model?

Yes, when the model, preprocessing, usable data and evaluation objective are available. Work can focus on errors, version comparisons or integration into client software.

Dedicated services and further reading

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Insights

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THE NEXT STEP

A system ready
for its next step?

Let’s discuss your project