IBM Introduces Watson Studio 2.0

In a Medium post today, IBM has announced the next phase of Watson Studio: its 2.0 release. The blog post is included below.

With IBM Watson Studio, IBM has been ushering its customers into the era of AI. We from offering management have been working diligently to fortify and build upon the power of Watson Studio in order to further accelerate the multi-cloud AI journey, which enterprises across the globe have embarked on.

Now, it is my distinct pleasure to introduce the next phase of Watson Studio: Watson Studio 2.0.

If you deploy Watson Studio in your enterprise today, you’re certainly aware of the capabilities it provides:

• To develop algorithms and train your models where your data lives
• Increase team productivity bringing your scientists (both visual and programmatic) to drive your initiatives from discovery to production
• Accelerating your data science life cycle, delivering workflow-driven data science, enabling Data Science Center of Excellence across your enterprise.

With Watson Studio V2.0: Bringing talent, process and platforms together, we have advanced our capabilities to accelerate your data science life cycle:

Explore your data at scale: Simplify and expedite data preparation

To date, working with over 85+ customers across different domains, it’s clear to me that 90% of the market is in the data science and business analytics (DSBA) segment and has not yet turned their focus to advanced machine learning and deep learning techniques to realize value; with majority of the time being spent on challenging and lengthy data prep process.

Connectors: With Watson Studio V2.0, we have advanced our capabilities to perform data exploration at scale:

• Adding 43 data connectors like Dropbox, Salesforce, Tableau or Looker.
• Asset Browser experience to navigate through Schemas, Tables and Objects.

Data Prep and Data Exploration:

With Data Refinery:

• Scientists can collaboratively review, refine and augment data sets visually
• Applying over 150+ advanced operations on data (to highlight a few below) with automation to run these reproducible refinery flows.

Running analytics at scale where your data lives:

With Execution Engine for Hadoop:

• We have enhanced our integrations with Hadoop Distributions (CDH and HDP), to run analytics where the data lives leveraging your existing compute, with improved performance, security and scale while maintaining quality and stability.
• Programmatically leverage Pyspark within notebooks to massively train on Spark nodes

Jobs and Scheduling:

• Built in batch and evaluation job management for Python/R scripts, SPSS streams and Data Refinery Flows in a single UI with multiple scheduling options.

Enhanced focus on citizen scientists:

• We have enhanced the IBM SPSS® Modeler as part of Watson Studio, improving the data exploration and visual modeling including integrated data mining and SQL pushback.
• Scientists can add several NEW nodes [Auto Data Prep and Auto Modeling, enhanced Data Visualization, Data Refinery integration and push to production], including 28 new features supporting data preparation and 44 capabilities (feature extraction and modeling techniques) to accelerate Machine Learning.

Enable governed collaboration:

With our governed collaboration, we’ve added a collaborative interface, similar to Slack, to our Jupyter Notebook integration. You can add notes within the tool segment itself as you have questions and as you discover new techniques you want to apply. This allows you to collaborate right where the discovery happens via governed collaboration guarding sensitive initiatives.

Openness with Enterprise Open Source:

To support programmatic scientists, when writing code:

– Scientists can import open source packages and libraries or bring their own.

– We make it easier for teams using open source tools while indemnifying packages and libraries bundled with the platform with appropriate security engineering practices to ensure machine learning pipelines are reliable and robust.

– Our entire platform is built using Kubernetes for orchestration, docker for containerization through instrumentation following infrastructure as code principles, with enhanced DevSecOps engrained with governance to support large enterprises.

Reproducibility and Versioning:

We’ve also enhanced version control integrations to make accessing and editing of different versions of assets more seamlessly considering reproducibility from the ground up:

– Support for major GIT frameworks — Github, Github enterprise, BitBucket and BitBucket server.

– No Vendor Lock-in, customers can download every asset they build using IBM Watson Studio and Watson Machine Learning by exporting their assets to their laptops or to GIT