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IBM Watson Studio AI

IBM Watson Studio empowers data scientists, developers, and analysts to build, run, and manage AI models and optimise decisions anywhere on IBM Cloud Pak for Data. Unite teams, automate AI lifecycles, and speed time to value in an open multicloud architecture.

IBM Watson Studio Ai Features

• Automatically build model pipelines. Prepare the data and select model types. Generate and rank model pipelines.

• Cleanse and shape data with a graphical flow editor. Apply interactive templates to code operations, functions, and logical operators.

• Create a notebook file, use a sample notebook, or bring your own notebook. Code and run a notebook.

• Prepare data quickly and develop models visually with IBM SPSS Modeller in Watson Studio.

• Build experiments quickly and enhance training by optimising pipelines and identifying the right combination of data.

• Bring your model of choice to production. Track and retrain models using production feedback.

• Combine predictive and prescriptive models. Use predictions to optimise decisions. Create and edit models in Python, in OPL, or with natural language.

• Monitor quality, fairness, and drift metrics. Select and configure deployment for model insights. Customise model monitors and metrics.

• Compare and evaluate models. Evaluate and select models with new data. Examine the key model metrics side-by-side.

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IBM Watson Studio AI

IBM Watson Studio empowers data scientists, developers, and analysts to build, run, and manage AI models and optimise decisions anywhere on IBM Cloud Pak for Data. Unite teams, automate AI lifecycles, and speed time to value in an open multicloud architecture.

IBM Watson Studio Ai Features

• Automatically build model pipelines. Prepare the data and select model types. Generate and rank model pipelines.

• Cleanse and shape data with a graphical flow editor. Apply interactive templates to code operations, functions, and logical operators.

• Create a notebook file, use a sample notebook, or bring your own notebook. Code and run a notebook.

• Prepare data quickly and develop models visually with IBM SPSS Modeller in Watson Studio.

• Build experiments quickly and enhance training by optimising pipelines and identifying the right combination of data.

• Bring your model of choice to production. Track and retrain models using production feedback.

• Combine predictive and prescriptive models. Use predictions to optimise decisions. Create and edit models in Python, in OPL, or with natural language.

• Monitor quality, fairness, and drift metrics. Select and configure deployment for model insights. Customise model monitors and metrics.

• Compare and evaluate models. Evaluate and select models with new data. Examine the key model metrics side-by-side.

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