Bangalore: An in-depth study on talent in the Machine Learning (ML) space by Zinnov, a global management consulting firm, revealed that while a few startups have a had success stories in their AI (artificial intelligence)/ML journeys, there still exists a deep chasm, and most startups and global enterprises haven't been able to succeed and/or scale, their ML initiatives. The AI/ML spend is predicted to touch $400 billion by 2020, according to industry estimates. Given this, it is more important for organizations to invest in the talent that will capitalize on this niche technology. However, acquiring and retaining, the right kind of ML talent continues to remain a significant challenge for organizations. Zinnov's study explained that a large contributor to this challenge is the skewed concentration of the niche ML talent.

The news of the recent Cambridge Analytica scandal did not just bring privacy concerns to the fore, it also signified how valuable a commodity data has become in today's information age. It showed us how impactful the process of leveraging data can be, and why companies across the board are adopting Machine Learning to learn from their data and completely transform their businesses. Despite the clear need, global enterprises and start-ups are struggling to scale their Machine Learning initiatives. One of the major factors limiting scale is the inability to acquire and retain the right talent. In a recent Zinnov Talent Hotbeds Forecast, we analyzed multiple cities for Machine Learning talent and predicted new hubs that would emerge over the next two decades. In the study, we found that Global top 500 R&D spenders together employ over 92,000 employees skilled in Machine Learning technologies and that 32% of this number are employed by the world's Tech Giants such as Google, Amazon, Facebook, and Microsoft.

Machine learning – a piece of the artificial intelligence constellation – holds a lot of promise for enterprises, enabling programs and algorithms to become ever more intelligent. However, there's one problem: even the best-educated humans need more learning before they can understand machine learning. Bob Hayes, a professional data scientist and keen observer of all things data, picked up on a survey by Kaggle that finds that even data scientists still have a grasp on machine learning. The survey "revealed that a limited number of data professionals possess competency in advanced machine learning skills," says Hayes. "About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%). Deep learning techniques were among the ML skills with the lowest competency rates."

Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning). While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning.

Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning). While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning.

Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning). While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning.