IBM Watson Studio

Automatically build model pipelines with AutoAI

Using AutoAI, you can build and deploy a machine learning model with sophisticated training features and no coding. AutoAI automatically analyzes your data and generates candidate model pipelines. These model pipelines are created iteratively as AutoAI analyzes your dataset and discovers data transformations, algorithms, and parameter settings that work best for your problem setting.

Bring your open source notebooks

The JupyterLab IDE, included in IBM Watson Studio, provides all the building blocks for developing interactive, exploratory analytics computations with Python. JupyterLab enables you to work with documents and activities such as Jupyter notebooks, Python scripts, text editors, and terminals side by side in a tabbed work area. You can create a notebook file, use a sample notebook or bring your own notebook to IBM Watson Studio.

Prepare, explore and refine data

Use the Data Refinery tool to cleanse and shape data with a graphical flow editor. Understand the quality and distribution of your data using built-in charts, graphs, and statistics. You can apply interactive templates to code operations, functions and logical operators. Schedule data flow executions for repeatable outcomes.

Run and train models

Build experiments quickly and enhance training by optimizing pipelines and identifying the right combination of data. You can choose from tools that fully automate the training process for rapid prototyping to tools that give you complete control to create a model that matches your needs.

Deploy and monitor models

Bring your model of choice to production. Track and retrain models using production feedback. Automatically generate APIs to build AI-powered applications. Streamline model management and deployment end-to-end with an easy-to-use interface.

Monitor quality, fairness and drift metrics

Monitors and manage your models to gain trust and transparency in AI. With model monitoring and management on a data and AI platform, an organization can monitor model fairness, explainability and drift, visualize and track AI models in production, validate and test models to mitigate regulatory and operational risks, increase end-to-end visibility of AI lifecycles.