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Build, deploy, test, retrain and monitor a predictive machine learning model.
Watson Machine Learning key features
Put the power of AI model deployment in your hands
Create a real-time object detection app
Use Watson™ Machine Learning to train your own custom model to detect objects in real time — without substantial computing power and time.
Build a product recommendation engine
Use Jupyter Notebooks with IBM Watson Studio to build an interactive recommendation engine PixieApp and deploy it with Watson Machine Learning.
Monitor model performance
Deploy and monitor machine learning models using German credit data with Watson Machine Learning and IBM Watson OpenScale™.
Automate model building with AutoAI
Build models, view a leaderboard, compare pipelines and deploy selected models with Watson Studio and Watson Machine Learning.
Predict a product purchase
Predict whether a customer is likely to buy a tent from an outdoor equipment store with AutoAI using Watson Studio and Watson Machine Learning.
Use a Python notebook to deploy decision optimization
Deploy your decision optimization model with Watson Machine Learning using a Jupyter notebook to access machine learning services and monitor jobs.
Watson Machine Learning use cases
Use case: Get started with AutoAI training
Problem Solution AutoAI helps data scientists rapidly develop candidate pipelines, select top performing models on the leaderboard and deploy models with Watson Machine Learning Cloud. Model monitoring is easier and faster, and the overall process can be reduced from weeks to hours.


Use case: Bring open source data science to production
Problem Solution With Watson Machine Learning Server, you can deploy a machine learning model to IBM Watson Machine Learning using the Python client. You can scale workloads and deploy assets with a seamless user experience.
Use case: Bring your Watson Studio Desktop to deployment
Problem Solution Watson Machine Learning Server can be connected to Watson Studio Desktop to provide computational power as a runtime, or as a deployment space with multiple user management.


Use case: Build and scale your AI models at scale
Problem Solution Together with Watson Studio, Watson Machine Learning Local helps you build, train and deploy models at scale in your private cloud or the public cloud of your choice including AWS, Azure, and others.
Use case: Deploy your decision optimization models
Problem Solution With Watson Machine Learning, you can deploy your decision optimization prescriptive model and associated data. This can be achieved using the Watson Machine Learning REST API or by using the Watson Machine Learning Python client.


Use case: Build and scale models on a data and AI platform
Problem Solution IBM Cloud Pak™ for Data, a cloud-native data and AI platform, together with Watson Studio Premium, delivers a seamless experience for running and optimizing machine learning and decision optimization models.
Try the free hands-on lab
Explore multiple machine learning and deep learning capabilities of IBM Watson Studio.