From Science to Art: Making Machine Learning Approachable

The high barrier to entry prevents many companies from tapping into the full potential of machine learning. But what if you could make it more accessible? 

We’re in the midst of a data explosion, with today’s enterprises amassing goldmines of information (25 quintillion bytes of data every day, according to some reports). But what exactly are they doing with this data? Considering the volume of data being collected is quickly becoming unmanageable, now is a good time to shift from manual machine learning to a cognitive approach. This enables businesses to better capitalize on their data and facilitate agile decision-making.

At this point, much of the discussion around machine learning has pivoted from adoption to how to simplify the adoption and implementation process. Many enterprises are looking to answer the question of how you break down the immensely tall barriers around data science so you can fully tap into the undeniable advantages machine learning has to offer.

Today, many businesses are simply collecting data, with little being done to translate it into usable intelligence. The data and people wind up trapped in siloes, and beyond that, any attempts at data analytics so far have usually been done on a limited scale. Generally speaking, these efforts were done with either one tool or one team, resulting in a very localized perspective of a much larger context.

For instance, a dashboard of results contains minimal traces of where insights have been sourced from, and a data table generated during one phase of a process may not be usable for any processes further down the stream. What enterprises actually need is for all involved users to be able to access the required intelligence so the necessary parties can leverage this insight to drive business goals.

From Inscrutably Scientific to Unbelievably Intuitive

The demand for machine learning is growing faster than ever before, and it’s currently one of the fastest growing disciplines of data science. Unfortunately, the barriers to entry in terms of cost and skill requirements are still as daunting as ever. This has led to a data scientist arms race, with enterprises frantically competing to woo, hire and retain expensive data scientists and engineers with fancy degrees to stay one step ahead. In fact, the number of job openings for machine learning engineers and data scientists far exceeds the availability—especially with so many already snapped up by industry titans like Google, Facebook and IBM.

So, where can you find these reclusive coders? It’s an understatement to even say it’s not an easy task.

But what if we flipped that equation on its head? Imagine if machine learning was no longer restricted to the world of genius-level data scientists and engineers—instead, it was open-source software that enabled non-coders and non-technical staff to access, build and deploy machine learning capabilities.

This would enable businesses to widen the practical application of machine learning to a much higher degree, while also lowering cost barriers. Everyone from developers to operations managers to business analysts to even business stakeholders would be able to cash in on the benefits of machine learning. 

You Don’t Need a PhD to Crack Machine Learning

We at the Progress DataRPM team believe that data science is not merely about the algorithms, it’s about the value that the algorithm generates. DataRPM democratizes machine learning and data science through an innovative platform that arms every employee in an organization—from frontline employees to the board—with seamless, complete intelligence. It also helps them leverage the power of cognitive analytics for existing business applications, while at the same time opening up opportunities for rapidly building cognitive applications.  

With this degree of accessibility, machine learning could spread to millions, or possibly even billions, of people. This means that companies no longer have to expend precious time and resources on attracting and hiring entire teams of expensive data scientists to write code. With pre-populated algorithms, parameters and configurations, you’ll eliminate the need for manual data science coding altogether. The machines themselves will be able to build models and predict outcomes, leaving your team free to spend more time analyzing and implementing the results.

With the cognitive approach to machine learning, several models can be built simultaneously, so processes that were once linear can now happen in parallel. This will not only save precious time, but also empower enterprises to amplify the scope of data investments. Deep, meaningful insights are extracted from each model and built by abstracting the required code, eliminating the need for manual coding. Thus, businesses can leverage the benefits of predictive analytics and insights while also monetizing their big data investments for a fraction of the time and effort they would’ve normally spent.