A deeper shade of simplicity at IBM Insight at World of Watson 2016
Simplicity is the most fundamental accelerator of focused action. If you can simplify your working environment without diluting your core capabilities, you can significantly boost your speed, productivity and effectiveness. When enterprises consider their investments in data and analytics solutions, simplification is absolutely essential to help control costs and focus on achieving desired outcomes.
Advancing the roadmap
At IBM Insight at World of Watson 2016, IBM announced and demonstrated several newly available solutions that are expected to help organizations toward achieving those objectives. These latest announcements represent a clear progression from earlier commitments announced in June in San Francisco, California and barely a month ago in New York City. Here is a quick overview of IBM Insight at World of Watson 2016 announcements made on day one of the conference and how they advance our ongoing roadmap to help clients make their data investments simpler and more effective.
IBM Watson Data Platform
This week, IBM announced the general availability of IBM Watson Data Platform (WDP). This is the name for the solution initially dubbed “Project DataWorks” in its September 27 unveiling in New York. Consistent with the announcement at that time, IBM WDP is a new open-source-based cloud data and analytics platform that simplifies and automates data-driven business innovation. It is a single cloud-based development platform for team data science which integrates all data for cognitively-powered decision-making. It provides a self-service task-oriented environment for teams of data scientists, data engineers, and other professionals to collaboratively develop, iterate, and deploy sophisticated AI, cognitive computing, machine learning, and other advanced analytics.
WDP enables data professionals to rapidly discover, collect, ingest, and organize data from all sources; share common datasets and models; and ensure strong data security and governance. It includes role-focused user experiences, which enable individual data professionals—such as data scientists, data engineers, and application developers—to put data to work in an environment built for their particular skill levels and responsibilities. WDP also includes solution Blueprints, which package the integration and smarts for specific scenarios—such as data lakes—thereby simplifying development and speeding time to value. Available now on IBM Bluemix, WDP integrates with Apache Spark, IBM Watson Analytics, and the IBM Data Science Experience. It is available under self-service and enterprise plans. Already, over 3,000 developers are working on the WDP and over 500,000 users have been trained on capabilities that form the basis of the platform.
IBM Watson Data Platform Plan
IBM also announced the expansion of WDP’s IBM WDP Plan, which provides easy, flexible access to a wide range of data and analytics services. It is a single package for accessing a wide range of data and analytics services that work natively with WDP, which is available for a single monthly subscription starting at $25,000 for 32 TB or a digital self-service plan starting at $50 per month for 20 GB.
Data Science Experience
Also demonstrated at IBM Insight at World of Watson 2016 was WDP’s DSX. Initially put into closed beta in June 2016 and open beta in September 2016, DSX is a single, integrated modeling tool for data scientists engaged in collaborative development with data engineers, business analysts and other data professionals.
Integrated closely with WDP, DSX is a cloud-based, self-service social workspace that enables data scientists to consolidate their use of and collaborate across multiple open source tools such as Python, R and Spark. It provides productivity tools to accelerate data scientists’ creation of cognitive, predictive, machine learning and other advanced analytics for cloud-based deployment. It also includes a rich catalog of learning resources for teams of data science professionals to deepen their understanding of tools, techniques, languages, methodologies and other key success enablers.
IBM Watson Machine Learning Service
Another announcement was the closed beta of a key WDP component—its Machine Learning Service. This Bluemix service, expected to be put into open beta by December 2016, is a comprehensive environment for automating the processes of training, optimizing and deploying machine learning models on WDP. It enables users to rapidly create self-learning cognitive models for incorporation—through published application programming interfaces (APIs)—into everyday business applications. It helps ensure that models are trained on the best data at any time, it automatically selects the algorithms well suited to be incorporated into models and it creates models automatically.
The Watson Machine Learning Service also enables data scientists to automate the retention, validation and management of models to help ensure that they don’t decay over time. Formerly code-named Project Moksha, the service is expected to enable data scientists and application developers to keep their models optimized without taking them offline from production applications. It will be offered as a stand-alone Bluemix service and as a WDP service that is integrated within DSX. The service will begin with Apache SparkML with additional algorithms included in the future and can be accessed through Watson Data Platform, as an API on IBM Bluemix or on z/OS.
Open ecosystem expansion
And IBM also announced at the conference the expansion of WDP’s open partner ecosystem. This expansion adds Keen IO, Quoble and Alation to a community that now totals more than 20 partners. As discussed in the 27 September 2016 announcement, these and future partners plan to certify their solutions to work with WDP. IBM also announced new programs with established partner Galvanize. These programs involve Data Maker Faires, Data Maker Spaces and Data Maker Incubators:
- Data Maker Faires: Community events that encourage collaboration to build data products
- Data Maker Spaces: Physical campuses that provide hands-on training with data design principles
- Data Maker Incubators: Start-up accelerators that provide complementary access to WDP