AutoML has been an essential part of our ever-changing world. With more and more organizations turning to AutoML, it makes us wonder if this would mean the obsolescence of Data Scientists.
AutoML platforms are used for automating AI and machine learning processes. First-generation AutoML platforms focused only on the machine learning part of the data science. But second-generation platform; AutoML 2.0 works a step further and aims at deriving new ways to make the most challenging part of the workflow, i.e., Feature Engineering automated which is the most time consuming and highly manual step of any data science workflow . This results in:
- More Automation for Data Science : With this new-found ability to “auto-generate” features, Business Intelligence teams can work with efficiency even with minimal help from data scientists.
- Democratization: Data science and automation can accelerate and automate the process of discovering and creating features, thus making it easy for a larger group of users to contribute to the data science process. AutoML 2.0 enables the business to expand its use of data science without having to hire data scientists, i.e., minimal investment and higher returns.
- Doesn’t replace but increases productivity: By making the toughest challenge of Feature Engineering automated, AutoML 2.0 has made data scientists’ work easier and efficient. The game-changing AutoML also enables data scientists to explore the so-called “unknown unknowns,” which no one has ever had the time for or expertise to do.
In conclusion, AutoML 2.0 is more of an enabler than a threat to the data scientists. AutoML 2.0 provides the necessary automation and acceleration to the data scientists, making them an indispensable part of the business.
#AIMonks #AI #Artificial Intelligence #AutoML #MachineLearning #DataScience #DataScientist #BusinessIntelligence
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