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.
What are the key capabilities of Data Science and Machine Learning?
Optimize AI and cloud economics
Put multi-cloud AI to work for business. Use flexible consumption models. Build and deploy AI anywhere.
Predict outcomes and prescribe actions
Optimize schedules, plans and resource allocations using predictions. Simplify optimization modeling with a natural language interface.
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.
Unify tools and increase productivity for ModelOps
Operationalize enterprise AI across clouds. Govern and secure data science projects at scale.
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.
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.