AI models that reach production and stay there

Building a model is the easy part. Getting it into daily use, keeping it accurate and being able to explain its decisions is where most projects stall. We help you set up the process that carries a model all the way.

What you gain

  • A repeatable process

    Models move from experiment to production through one pipeline, the practice known as ModelOps, not through a new effort each time.

  • Decisions in real time

    Models are exposed as services, so scoring, risk assessment and next-best-offer decisions happen while the customer is still there.

  • Explainable results

    You can show how a model reached its result and check it for bias, which regulators and customers increasingly ask for.

  • Lower model risk

    Automated validation and monitoring catch a model that drifts before it causes harm.

Data science is a team sport

Successful organisations do not depend on a few exceptional individuals. They build teams where data scientists, developers and analysts work on the same platform with the same data.

We help you set up that environment:

  • Open source and visual tools together. Jupyter notebooks, Python, R and Scala work side by side with visual, drag-and-drop model building.
  • Decision optimization. Optimization models are selected, deployed and shared through dashboards.
  • Governed projects. Access, versions and approvals are managed, so work can be audited and reused.
  • Deployment anywhere. The same model runs on premises or in any cloud.

For banks: Analytics-in-a-Box

Analytics-in-a-Box for Banking, our platform built on IBM Software Hub (formerly IBM Cloud Pak for Data), includes this data science environment together with a banking data model, governance and ready modules for customer insights, financial reporting and regulatory reporting. Read more under Data & AI.

Technologies we work with

Let's talk about data science

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