The AI Team: IBM Offers Free Assistance from Team of Data Scientists to Boost New AI Platform

Globally, demand for data scientists was projected to exceed supply by more than 50% by 2018, according to the McKinsey Global Institute. Most of the savvy tech giants have their own approach to this conundrum - Cisco, for example, has been training up a new generation of data scientists. For IBM, a key part of their plan is investing in AI and machine learning. By focusing resources on building a solid AI foundation, IBM is creating a set-up which will allow fewer data scientists to do the work of many.

Enabling AI

This February, IBM announced a new data science and machine learning platform, Cloud Private for Data. The platform is designed to radically simplify data collection, organisation and analysis, and works as an application layer deployed on the Kubernetes open-source container software. The system is streamlined to facilitate speedy data science, data engineering and app building, and uses an in-memory database to query data in a computer’s Random Access Memory (RAM) instead of querying data from physical disks. This means that query response times are shortened dramatically, allowing analytics to support fast-paced business decisions.

Christian Rodatus, CEO at IBM business partner Datameer, said in a statement: “Two of the biggest challenges for data scientists are cleansing and shaping data, and operationalising their insights to deliver value to business. The direction IBM is headed with IBM Cloud Private for Data will enable clients to prepare data for machine learning and AI projects more quickly and operationalise these across the business.”

By formulating the fundamental architectures of a scalable AI system early on, IBM is seriously cutting down the amount of work to be done further down the line. With the talent crunch well underway for those recruiting data scientists and analysts, this move towards more efficient AI is a smart and sustainable response from the technology giant.

To help ease users into the new AI platform, IBM has also introduced a team of 30 experts on data science and machine learning: the Data Science Elite Team. Over the coming years, the team is expected to grow to 200, and will continue to provide a service which IBM describes as a “no-charge consultancy dedicated to solving clients’ real-world data science problems and assisting them in their journey to AI”.

Patricia Maqetuka, Chief Data Officer at the South African bank, Nedbank, shared her experience working with Data Science Elite Team: “Nedbank has a long tradition of using analytics on internal, structured data. Thanks to IBM Analytics University Live, we were exposed to the guidance and counsel of IBM’s Elite team. This team helped us to unlock new paradigms for how we think about our analytics and change the way we look at use cases to unlock business value.”

Machine Learning Hubs

Machine learning is poised to change the way we automate processes forever, and it’s likely that it won’t be too long before data science breaks free from code dependence. Langley Eide, Chief Strategy Officer at Alteryx explains, “In 2018, we’ll see increased adoption of common frameworks for encoding, managing and deploying Machine Learning and analytics processes. The value of data science will become less about the code itself and more about the application of techniques”.

IBM are keen to be at the forefront of this movement. Their first machine learning hub was built in Silicon Valley in February of 2017, and was designed as a space for IBM’s machine learning specialists and data scientists to share their expertise with other businesses. The mission of the hub was to close the gap between available open-source tools and the knowledge required to use them. A month after the first lab was opened IBM expanded the project, building hubs in Toronto, Beijing, and Stuttgart, and in August a fifth was opened in Bangalore.

In these hubs, machine learning experts work with companies over the course of a three-day workshop to implement initial prototypes. Data scientists collaborate with businesses using tools like Data Science Experience (DSX) to find tailored solutions for brands in a range of industries, including travel, energy and utilities, healthcare, financial services, manufacturing, and retail industries.

Data scientists and businesses walk through the stages of the machine learning process together, until they create concrete results. As well as being a place for businesses to upskill their employees, the data scientists and machine learning engineers who work at the Machine Learning Hubs also write academic papers and contribute to open source projects, which sustains a thriving community of collaborative knowledge.

Gartner predicts that more than 40% of data science tasks will be automated by the year 2020 (Gartner.com).The growth of machine learning is a cornerstone of this development, and has already been astonishingly rapid. The first one-click data-in-model-out platform appeared just over a year ago, and others have been popping up ever since – around seven or eight competitors exist now, including Xpanse Analytics, PurePredictive and DataRobot.

Bill Vorhies, Editorial Director for Data Science Central writes,

“These AML platforms have achieved one-click-data-in-model-out convenience with very good accuracy. Several of these vendors have also done a creditable job of automating data prep including feature creation and selection.”

The efficiency with which machine learning can reduce data prep time creates huge opportunity for data analysts to focus their time on more broadly perfecting the application of techniques rather than the coding itself- and IBM are keen to lead the way.

Make sure to download the Data Leaders Summit agenda to check out all of the great activities, speakers, and sessions planned for this year.