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Deploy and run AI models with Watson Machine Learning
As part of IBM Watson® Studio, IBM Watson Machine Learning helps data scientists and developers accelerate AI and machine learning deployment on IBM Cloud Pak® for Data. Deploy AI models at scale across any cloud on an open, extensible architecture. With IBM you can: Unleash the power of Watson AI technologies. Automate the AI lifecycle for ModelOps.
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Watson Machine Learning simplifies and speeds deployment at scale
Learn the economic impact of Watson Studio and Watson Knowledge Catalog
Moving beyond experimentation and maintaining production-level accuracy of AI models are still big challenges. Watson Machine Learning can accelerate the time to value of any model, with a projected ROI of 459% over three years, with payback in less than six months.¹
51%
find optimizing, sustaining and expanding AI capabilities challenging²
80%
of AI pioneers are investing in the process to train algorithms³
Watson Machine Learning features
What’s new with Watson Machine Learning

On-demand webinar: What’s new with data science
See how you can deploy deep learning and ML models anywhere.

AutoAI playlist: Watson Machine Learning deployment
Watch tech experts in AutoAI videos, take product tours and explore hands-on labs.

ESG white paper: Predict and Optimize
Read the analyst testing of Watson Studio on IBM Cloud Pak for Data.
Watson Machine Learning in action

Anyline
Train and deploy deep learning models for offline optional character recognition (OCR) on mobile and embedded devices in record time.

Caixa Geral de Depósitos France
Accelerate customer service by delivering faster decisions using an innovative hybrid cloud application enabled by machine learning.

KIST Europe
Explore how machine learning can automate quality management processes, reduce defect rates and save costs.
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See pricing options and the latest news and details to start developing machine learning models today.
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Footnotes
² 2019 Forrester Infographic “AI Experiences A Reality Check” (link resides outside IBM) ³ 2018 MIT Sloan Review (link resides outside IBM)