Artificial Intelligence is reshaping our business and society. These very powerful algorithms are changing our perception of reality while catering to our instincts, needs, and wants. Still, many companies are slow in adopting the AI technology itself. So, one may ask that as AI is becoming ubiquitous, what would it take for us to implement it across the board?
As a firm believer in analytics, I have made it my personal goal to communicate with fellow analysts to immediately understand how AI will fit in into their daily operations. Interestingly, no matter whether they’re from IT, Marketing, Supply Chain or Finance, they all acknowledge that AI will affect their future work at their respective companies and will drive change within or outside their enterprise. However, sadly enough, they find the application aspect of AI (building and deploying Machine Learning & Deep Learning algorithms) pretty daunting and still rooted deep within lines of intricate code and mathematics.
To be honest, you can’t really blame them for being so intimidated. To be a long-term asset for their employers, these analysts need to learn the art of blending data from disparate sources, spreadsheets, databases (relational and NoSQL), and social media. They need to learn cleaning, summarizing and pre-processing the data before writing the code to find the best predictive model for their data.
There are dozens of algorithms like Decision Trees, Regression (classical and regularized) and, Neural Networks (Classical and Deep Learning). There are also dozens of open-source AutoML libraries and ensemble algorithms that data analysts rely on to deliver the best results. Unfortunately, these analysts fail to understand that AI is not just about finding the best model but, also about solving the problem in the most efficient manner by having a proper pipeline. As a result, these analysts go through a set of resources, that lack customization and personalization, and spend weeks trying to find the best model at the expense of solving the problem quickly and effectively.
Isn’t this overwhelming? I’m sure it is.
Currently, there are about millions of data analysts that lack the necessary skill set and problem-solving ability. They’re really good users of BI tools but do not have a competitive edge when it comes to predictive analytics. In order to do so, a lot of data analysts are taking multiple online courses and are spending their energy, hours and money on something that require a deeper set of expertise to master. As a matter of fact, these online courses just equip these data analysts with the necessary tools to survive, but not to thrive. Activating predictive analytics and unlocking the full potential of AI are complicated and time-consuming tasks. From data preparation to data modelling to deployment, an analyst needs technical skills and efficient decision-making ability which are not that simple to obtain.
So, what do we do?
Firstly, we need to realize that AI is changing society at an alarming rate so speed is key. Secondly, we need to realize that as time is short, smart decision-making skills are of utmost importance. Thirdly, coding isn’t the only skill required and problem-solving skills are also equally essential. Lastly, there is no time to build things from the elementary stages. This will be intimidating and definitely will not win us the race. We’ll keep on playing catch-up!
So, in light of all these issues, we at mltrons are building AI-powered predictive analytics (AutoML & AutoDL) platform specially catered to the needs of the data analysts. Our mission is to help these data analysts evolve into data scientists without going through a lot of technical training or coding. We envision a world where everyone, no matter what their role, understands artificial intelligence and uses it to improve their decision making. After all, life improves with good decisions.
If you’re interested in beta testing of our product, head onto our website and sign-up now!
If you have any comments or suggestions, reach out to us at email@example.com. Thank you
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