Organizations are now tapping data science and artificial intelligence (AI) as a technology-enabled business strategy. Their practices have evolved to a point where organizations of all sizes are actively experimenting to inject predictive insight into business. Experimentation is accelerating across multiple clouds. The need for speeding through data preparation and exploration, modeling and training has never been higher. Yet, moving from experimentation to production has remained a challenge.
To simplify the path toward enterprise AI, organizations are turning to IBM Watson Machine Learning and IBM Watson Studio. Together these innovative solutions bring a concrete answer to the needs of companies to grow and drive business by infusing Artificial Intelligence with efficiency in their daily tasks and processes.
You can create, train, and deploy machine learning models and neural networks with Watson Machine Learning using multiple tools in IBM Watson Studio.
IBM Watson Studio
Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business.
It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.
IBM Watson Studio empowers organizations to tap into data assets and inject predictions into business processes and modern applications and then optimize business value with visual data science and decision optimization.
With Watson Studio, build and train AI models and prepare and analyze data in a single integrated environment
IBM Watson Studio is a collaborative environment with AI tools that you and your team can use to collect and prepare training data, and to design, train, and deploy machine learning models.
Ranging from graphical tools you can use to build a model in minutes, to tools that automate running thousands of experiment training runs and hyperparameter optimization, Watson Studio AI tools support popular frameworks.
Uncover hidden insights in your data with the Data Refinery tool, which provides built-in data cleaning and transformations.
Using the included dashboarding service, produce stunning visualizations directly from your data in real time.
Test and deploy models, using customizable compute environments that scale up and down with your workflow.
Improve your model’s performance by visualizing fit between model and data using the capabilities of IBM SPSS Modeler.
Once your model is ready, deploy and score it with the available Watson Machine Learning service.
IBM Watson Machine Learning
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.
Using IBM Watson Machine Learning, you can build analytical models and neural networks, trained with your own data, that you can deploy for use in applications.
Take benefits from the usage of IBM Watson Machine learning regarding :
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.
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.
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.
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.