Mythbuster: With Auto ML, No need for Data Scientists

There are a lot of talks these days about Auto Machine Learning but does it really powerful enough to eliminate the need for hiring Data Scientists? Some companies believe that they don’t actually need data scientists at all – they can instead use auto-ML to do all the heavy lifting. But be cautious of such claims, because we will tell you why hiring a data scientist is key to making the right data-driven decisions in your business.

What is Auto Machine Learning?

Auto Machine Learning is a type of machine learning that is deployed without the need for human data scientists.

Auto Machine Learning works by using a set of pre-defined rules or algorithms to analyze data and make predictions. This process is repeated over and over again until the system reaches a satisfactory level of accuracy.

Some of the key tasks that Auto Machine Learning can perform are:

  1. Data Processing as well as pre-processing
  2. Feature Engineering
  3. Model selection
  4. Hyper Parameter Tuning
  5. Initial Architecture
  6. Model Output

Auto Machine Learning has many advantages over traditional data science methods. First and foremost, it is fast and efficient – meaning it can quickly identify patterns in large datasets. Second, it is cost-effective – because there is no need for human labor to manually input data into the system. And finally, it’s scalable – meaning it can be implemented in multiple locations across an organization without affecting performance or reliability.

Why Hire a Data Scientist?

It is understandable that you have a real business problem and you think, you could use an Auto ML tool where it is pre-built then let us break it to you, but that might not be in your favor long-term. Here’s why. When a business problem is dealt with, such as fraud detection in a fintech company, where there are many transactions, it is important to follow a framework that yields guaranteed results.

We follow BADIR Framework, where we first focus on the business question first, that is what type of fraud are we trying to detect, what are the constraints, etc. Framing the right business question is very important, which requires expertise from different departments and Auto ML will fail at providing such accuracy in results.

The next step is to define the analytics goal, that is building a hypothesis as it is important to keep the focus on the main business question as it’s easy to get lost under the load of data that would be available at your fingertips. And most valuable assumptions that could be made by a data scientist, would be missing while using Auto ML.

Collecting data and validation could use some automation but rely entirely on it could not be functional. And perhaps, deriving insights part is where automation could be handy. That is if you want to build a base model, then using AutoVecka would be easy. But other than that, when implementing feature engineering or such complex methodologies, then a data scientist because essential to the process.

Now when it comes to the recommendation that is supposed to be derived from the insights, the accuracy and focus on the problem are very important to address them. And you find the results to be faulty or vain if you are only using Auto ML and deployment of the results as well would be nearly impossible.


In the end, it all boils down to how much time and effort you want to put in for your business. Are you ready to go through this process? If yes, then hire a data scientist and prepare for some amazing results that will amaze users.

You can also be more hands-off by using auto-machine learning tools. But remember what we said before—they don’t work well if you don’t train them properly! In case the model doesn’t give enough accurate results, just adjust its parameters until the right decision is made.

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