In a similar way, suppose you are working on a complex model for fraud detection. It would make sense to dramatically accelerate training on Power machines leveraging GPUs and the high speed NVLink connections (see https://www.ibm.com/blogs/systems/ibm-power8-cpu-and-nvidia-pascal-gpu-speed-ahead-with-nvlink/), but then you might want to deploy the model right into a CICS in-transaction call on the mainframe for sub-millisecond scoring of a credit card transaction. These use cases need maximum interoperability and a unified user experience across diverse environments.

Fit-for-purpose ML is a tall order, but certain core principles apply, including bringing machine learning capabilities native to a variety of platforms and systems. You can deploy your machine learning models where it makes the most sense, whether that’s in the public cloud, behind the firewall, in the optimized IBM Integrated Analytics System, or on the mainframe, just to name a few. Further, our Kubernetes based ML environment can be stood up on infrastructure from a variety of popular Cloud vendors, giving you the ability to deploy to IBM and non-IBM cloud environments and enabling a multi-cloud strategy.

This keeps customers from working against data gravity: We let them bring the tools, intelligence, and analytics to the data rather than forcing them to drag data slowly — and insecurely — from place to place.

An Enterprise Platform for ML

We brought all these capabilities together into Data Science Experience (DSX), IBM’s enterprise platform for ML.

Perhaps it goes without saying, but the strategies and capabilities described above don’t stand alone. Working with IBM means the ability to integrate with the broader IBM portfolio, which ranges from master data management to governance to decision optimization to Watson Explorer to Watson Cognitive APIs to PowerAI and more. Combining these sets of capabilities doesn’t just create incidental efficiencies; together they can truly transform your organization. As an example use case, Watson Image Recognition produces a set of features that feed into downstream custom Machine Learning models in DSX that predict building energy efficiency. In a second use case, integration between Decision Optimization with CPLEX and DSX brings advanced store staff scheduling, going beyond linear programming to quadratic functions. In a third use case, Watson Speech-to-Text and advanced NLP capabilities in Watson Explorer (WEX) integrate with DSX to combine unsupervised and supervised techniques for understanding deep intent in customer calls into the call center.

And also..

We have integration not just with the IBM portfolio but also with a rich ecosystem of partners like H2O, HortonWorks etc. For example, integration with the HortonWorks Data Platform allows you to leverage native capabilities including browsing for data in the Hadoop cluster and authentication using Apache Knox.

Another strength is a steady stream of innovation coming in from IBM Research, ranging from automatic feature engineering to cognitive databases to distributed deep learning for scaling acceleration and more.

But in the midst of all the features and tools that IBM is providing to drive data science for the enterprise, it can be easy to forget the deeper reason our customers look to IBM in the first place. They look to IBM because they know that our decades of real-world experience across every imaginable industry means we bring an unmatched depth of knowledge about how organizations run and what makes them thrive.

As powerful as machine learning is, it’s a myth to think that organizations can throw data into a black box and get back actionable results. Doing feature engineering and building models and infusing them into the applications and processes of a business needs an intimate knowledge of the business and its dynamics — both at the high level of the industry sector and at the low level of transaction flow, regulation, and so on. That’s what differentiates IBM. Our industry-leading approach to machine learning is just the latest manifestation of a long tradition of helping customers find the future.

Check out these capabilities in the Data Science Experience offering — either on the cloud at https://datascience.ibm.com or behind the firewall with Data Science Experience Local.