Addressing the tech skills gap could be key to the future of machine learning

Dr. Greg Benson discusses the future of Machine Learning and the skills gap that could be hindering its advancement.

Far from the ‘Dark Fate' the latest Terminator movie would have you believe, Artificial Intelligence (AI) and its cousin—Machine Learning (ML) — are not the precursor technologies of the apocalypse. Instead, these technologies represent an exciting avenue of discovery and innovation that are transforming business and paving the way for the development of more sustainable practices alongside increased business efficiencies.

From delivering instant access to patient information in the healthcare industry, to streamlining production in intelligent factories, the application of ML technology has already had a tangible effect on enterprise. And, given adequate time and funding to mature, ML has the ability to become a truly game-changing technology. However, like many disruptive technologies, ML's future is not certain and it faces a number of challenges moving forward, not least of which is a lack of highly-trained ML practitioners available to train future generations of ML enthusiasts.

To learn more about the current state of ML within the enterprise and talk about the challenges it faces, we spoke with Dr. Greg Benson, Professor of Computer Science, San Francisco, and Chief Scientist at SnapLogic. Below is a lightly edited copy of our conversation where he highlights the importance of addressing the data science skills gap and discusses the barriers companies have experienced trying to adopt and deploy ML solutions.  

What barriers have companies experienced when trying to adopt ML?

In my view, there are three categories of barriers that companies face when trying to adopt or leverage ML. First, simply getting the type of data needed to train ML models can be difficult. In some cases, the data may be available, but because of either technical challenges or bureaucratic hurdles, data scientists cannot get the data that is needed. Alternatively, the data needed for training may not even be collected in the first place. Lots of interesting data is simply not collected and stored for later use. In this case, ML can't start until a process is in place to capture useful data, such as various forms of user activity or related event data. 

Second, we've all heard about the shortage of data science and ML talent. That is, there aren't enough data scientists with adequate ML knowledge to satisfy the demand from industry. I believe this is going to change as we have seen an increase in the number of Data Science programs, like the Master of Science in Data Science at the University of San Francisco, where I'm a professor. Also, ML is becoming more prominent in undergraduate computer science programs. Our educational system are responding to help meet the demand. 

Third, even with good data sets and qualified data scientists, there is a gap between building useful models and putting them to use in production. Larger tech companies, like Google and Apple, have built custom ML infrastructures to support the development and deployment of models so they can be used in applications, but most smaller companies don't have that kind of infrastructure. We are seeing quite a few platform solutions to this problem. For example, SnapLogic provides a self-service visual interface to put ML models into production.

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