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Four Myths about Machine Learning

We’ve talked about how machine learning is better than statistical models and how DataRobot supports the advanced machine learning techniques used in the insurance industry, today. But since it’s been around for generations, people are often left misconceptions about it.


Let’s clarify four popular myths about machine learning, right now:

Myth #1: “Machine learning is opaque.

This perception is due, in part, to simple familiarity – it’s easier for people to understand techniques they already know. It is also due to the greater complexity of machine learning models; machine learning models deliver more accurate predictions because they fit our complex world better than statistical models. Moreover, there are graphical tools available today that make it easy for a business user to see how a machine learning model behaves, and tools that deliver explanations for each prediction.


Myth #2 – “We will never be able to hire enough experts to use machine learning.”

Modern machine learning tools are much easier to use today than just a few years ago. Your organization already has domain experts: actuaries, claims managers, customer service reps, marketing managers, and underwriters. With the right tools and training, anyone can contribute to machine learning projects.


Myth #3: “Machine learning projects take forever.”

Managers recently surveyed by Gartner said that time to value is one of their biggest problems with machine learning. Those managers reported that it takes an average of 52 business days for their team to build a predictive model. However, modern machine learning software is faster than was in the past. It works on distributed platforms so that you can harness more computing power. With deployable code and prediction APIs, getting models into production takes a lot less time.


Myth #4: “Machine learning is a headache for IT.”

Modern machine learning platforms reflect enterprise computing standards. They work with widely used data platforms, conform to security standards, and run efficiently on commodity hardware. The best examples leverage open source software for ease of integration and low cost of ownership.


Accelteam has been actively working with customers from various industry segments driving business improvements using machine learning solutions like Datarobot. Be it a customer with or without data scientists in their organisation, Accelteam has the right solution to meet the customer’s expectations in their machine learning and AI quest.

In fact for the past two decades, AccelTeam has been one of the leaders in providing data-driven analytics solution in this region. If you would like to know learn more about DataRobot and machine learning solutions, please reach out to us at