Free hands-on lab: Machine learning and deep learning made easy with IBM Watson Studio
Why Watson Machine Learning? Why Now?
Data science and artificial intelligence (AI) practices have evolved to a point where organizations of all sizes are actively experimenting to inject predictive insight into business. Yet, moving from experimentation to production has remained a challenge. IBM Watson Machine Learning helps data scientists and developers work together to accelerate the process of moving to deployment and integrate AI into their applications. By simplifying, accelerating and governing AI deployments, it enables organizations to harness machine learning and deep learning to deliver business value. Integrated to work with Watson Studio, Watson Machine Learning empowers your cross-functional team to deploy, monitor and optimize models quickly and easily. APIs are generated automatically to help your developers infuse AI into their applications in minutes. Watson Machine Learning’s intuitive dashboards make it simple for your teams to manage models in production, and its seamless workflows enable continuous retraining to maintain and improve model accuracy.
Watson Machine Learning benefits
Scale
Watson Machine Learning makes it easy and cost-effective to deploy AI and machine learning assets in public, private, hybrid or multicloud environments. Seamlessly scale up your AI initiatives, growing pilot projects into business-critical enterprise deployments without large up-front investments.
Speed
Get AI assets to market faster by streamlining the model training and deployment process. Watson Machine Learning automates many aspects of model training, while multiplatform hardware optimizations accelerate training schedules by maximizing resource utilization.
Simplicity
Reduce skill shortages by taking advantage of a host of pretrained models and open data sets. Simplify lifecycle management with automated performance monitoring and continuous feedback, and interoperate easily with other data science tools with an open, modular architecture.
Watson Machine Learning features
Push algorithms and analytics to data
Decentralize and distribute your model training by harnessing Apache Spark to train machine learning and deep learning models on structured and unstructured data—whether it resides in relational databases, Hadoop and object storage.
Deploy and manage models
Manage and govern the AI and machine learning lifecycle from end to end, building portable models that can be deployed on cloud or on premises. Import models from other data science tools, and continuously train and deploy them as services, apps or scripts for a wide range of platforms and tools.
Augment and automate machine learning
Automate hyperparameter optimization and feature engineering to enable rapid training. Harness A/B testing and performance monitoring to create a feedback loop for retraining to keep accuracy as high as possible.
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Hands-on Lab: ML/DL made easy with Watson Machine Learning
