Introducing IBM Watson Studio and IBM Watson Machine Learning 2.0

Julianna DeLua
May 8, 2019 · 3 min read
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To continue our mission of putting AI to work for business, IBM has been making strides toward increasing prediction accuracy, automating tasks and optimizing outcomes in recent years. Now, more than ever, driving value from AI investments such as improving customer experience, mitigating risks and compliance, and streamlining operation, has become more readily accessible for any enterprise. After incorporating the extensive feedback from our clients and bringing some of the new innovations across IBM, we are pleased to announce the general availability of IBM® Watson™ Studio and IBM Watson Machine Learning (WML) 2.0. With this release, IBM is extending its leadership in ModelOps.

With this release, IBM advances our leadership in harnessing the power of AI for the hybrid multicloud era — connecting public clouds, private cloud, on-premises and desktops. That is, executing against our vision of enabling enterprise AI across multiple clouds at scale, speeding time to value and driving simplicity for all contributors whether you’re just getting started or deploying at scale. We remain focused on empowering a business to blend human savvy and machine learning to drive better results.

Building upon the success of the first release, Watson Studio and Watson Machine Learning 2.0 helps an organization:

Explore data at scale and deploy models virtually anywhere, and in any cloud

  • Experiment in the cloud and deploy anywhere in private clouds with a unified environment including find and share data by cataloging assets
  • Learn once and evolve with an improved user experience (UX) regardless of skills in a distributed team —more seamlessly integrating IBM SPSS® Modeler and coding interfaces with easier project export
  • Scale analytical tasks with Watson Studio and better organize machine learning with deployment spaces showing deployment status, the deployable assets, associated input and output data, and the associated environments.
  • Navigate through the schema, tables, or buckets in the data source to add connections to projects across IBM cloud services, other cloud services and on-premises databases

Accelerate time to value by reaching and maintaining production-level model accuracy

  • Evaluate, re-train and learn on demand across different WML deployment spaces suited for modern applications.
  • Quickly find optimal hyper parameters for deep learning models and train neural networks using the experiment builder.
  • Accelerate inference and training in a multi-tenant distributed model with the WML Accelerator.
  • And improve application portability for internal private clouds with the IBM v4 Application Programming Interface (API).

Simplify data science onboarding and deployment with productivity and automation tooling

  • Prepare and shape data using a GUI and code-based approach with IBM Data Refinery with more than 150 operations including Avro, JSON, Parquet, and text files, as well as CSV files.
  • Simply add connections to a broad array of over 40 data sources in projects and catalogs
  • Get integrated access to the Jupyter gateway from the Apache Hadoop execution engine.
  • And further streamline modern application interfaces with the Watson Studio Premium Add-on (formerly Data Science Premium Add-On) for IBM Cloud™ Private for Data.

With the release of V2.0, IBM is making it easier for businesses to complement human-led expertise and innovation with machine-generated insights. You can read more on Watson Studio and Watson Machine Learning V2.0 by Vishnu Alavur Kannan and Greg Filla respectively on this medium channel. Or, you can read the Enterprise Strategy Group (ESG) technical validation to learn more about our platform.

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