Data Science and Machine Learning

Predict and optimize outcomes with AI and machine learning models.

We are providing you with the environment and tools to solve your business problems by collaboratively working with data. You can choose the tools you need to analyze and visualize data, to cleanse and shape data, or to build machine learning models.

Data Science and Machine Learning

What are the key capabilities of Data Science and Machine Learning?

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Optimize AI and cloud economics

Put multi-cloud AI to work for business. Use flexible consumption models. Build and deploy AI anywhere.

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Predict outcomes and prescribe actions

Optimize schedules, plans and resource allocations using predictions. Simplify optimization modeling with a natural language interface.

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Synchronize apps and AI

Unite and cross-train developers and data scientists. Push models through REST API across any Cloud. Save time and cost managing disparate tools.

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Unify tools and increase productivity for ModelOps

Operationalize enterprise AI across clouds. Govern and secure data science projects at scale.

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Deliver fair, explainable AI

Reduce model monitoring efforts by 35% to 50%. Increase model accuracy by 15% to 30%. Increase net profits on a data and AI platform.

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Manage risks and regulatory compliance

Protect against exposure and regulatory penalties. Simplify AI model risk management through automated validation.

How can Data Science and Machine Learning improve your business processes?

  • Auto AI for faster experimentation
    Automatically build model pipelines. Prepare data and select model types. Generate and rank model pipelines.
  • Advanced data refinery
    Cleanse and shape data with a graphical flow editor. Apply interactive templates to code operations, functions and logical operators.
  • Open source notebook support
    Create a notebook file, use a sample notebook or bring your own notebook. Code and run a notebook.
  • Integrated visual tooling
    Prepare data quickly and develop models visually with IBM SPSS Modeler in Watson Studio.
  • Model training and development
    Build experiments quickly and enhance training by optimizing pipelines and identifying the right combination of data.
  • Extensive open source frameworks
    Bring your model of choice to production. Track and retrain models using production feedback.
  • Embedded decision optimization
    Combine predictive and prescriptive models. Use predictions to optimize decisions. Create and edit models in Python, in OPL or with natural language.
  • Model management and monitoring
    Monitor quality, fairness and drift metrics. Select and configure deployment for model insights. Customize model monitors and metrics.
  • Model risk management
    Compare and evaluate models. Evaluate and select models with new data. Examine the key model metrics side-by-side.

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