Will AutoML take away the Data Scientist jobs in the Future?
Introduction
Over the past few years, lots of machine learning modules have been introduced. These modules often automate various operations that otherwise would take a lot of time when data scientists do it manually. These machine learning (ML) models have achieved remarkable sophistication and effectiveness over time. Automation helps in reducing tedious and cumbersome jobs. According to Gartner’s report, more than 40% of data science jobs have become automated. The majority of the automation credit in data science goes to machine learning. But the question is whether this automatic machine learning (AutoML) will one day take away all data scientist jobs in the future. This article is a myth buster and will reveal what’s true and what’s not.
What is AutoML?
Automated machine learning, popularly known as AutoML, is the mechanism that involves machine learning to automate time-consuming, monotonous, and iterative tasks of data science and machine learning through machine learning models. It requires skilled programming precision and efficiency for data scientists and ML engineers to develop automated machine learning models. By eliminating tedious and repetitive large tasks, data scientists can increase their productivity while maintaining a quality model.
AutoML has become the new trend of work in the data science and machine learning domain. It aims to automate various time-consuming (manual) parts of data science, machine learning, and deep learning jobs of the overall machine learning pipeline. With the advancement in AutoML, data scientists are leveraging its potential to apply machine learning to real-world concerns that are highly applicable in the industry. Since data scientists and other industry leaders envision that machine learning is the key to future enhancements, automating through ML can reduce the development time in analysis, research, and execution. Some well-known AutoML tools are AutoKeras, Google Cloud AutoML, TPOT, H2O.ai, AutoViML, Microsoft Azure AutoML, etc.
Here’s a flowchart of where AutoML stands:
Will AutoML take away the data scientist jobs?
AutoML is just a trending tool that automates repetitive tasks that would, otherwise, take a lot of time when data scientists do them manually. This statement clearly shows that data scientists write these automated machine learning algorithms to do a portion of their entire data science & machine learning pipeline of work. Therefore, those who think that AutoML will replace a data scientist’s work and turn this job profile obsolete in the future are all absolutely wrong.
People started thinking the same because AutoML has proven time & again that it is the best way to automate a lot of industry work and data science operations. But, without data scientists, it will be nearly impossible to design such tasks. Also, what AutoML does is a portion of the whole data science and machine learning workflow pipeline. Hence, the data scientist’s role is not going anywhere in the future.
AutoML vs. Data Scientists
The Future of Data-driven AI requires Data Scientists
AI requires data to understand how the human world works and adapt to this ecosystem for beneficiary reasons. That’s where data scientists will play a significant role in designing machine learning models. Data scientists will logically analyze and look for hidden insights from granular data. AutoML cannot do that. To train the machine learning models to perfection, data scientists have to extract valid correlations and hidden data acuities & feed correct data to the ML pipeline. There are a lot of significant data science parts within the pipeline that cannot be fully automated.
Such tasks require deep knowledge of what the business wants from those data and design it the way business managers and decision-making leaders desire. Only data scientists can develop such solutions that align the data with the business requirements. Apart from all these, data scientists are also responsible for accomplishing complicated and intriguing tasks of running machine learning models that are not in the scope of AutoML. Data scientists often develop AutoML algorithms (as per their requirements) so that these algorithms can increase their overall productivity by automating manually-driven tasks that are time-consuming in nature. Data scientists cater to business-oriented insights by training machine learning and deep learning models with filtered data.
Wrapping Up
AutoML is a part of the entire data-driven & ML pipeline. AutoML cannot do even half the tasks that a data scientist can. Therefore, from this article, we can conclude that we cannot compare AutoML and Data scientists. Also, it is not appropriate to say that AutoML will take away or replace data scientist jobs in the future.
