Will AutoML take away my job? What is it?
By: Prejith Premkumar
Tags: Data Analysis, Machine Learning, Data Analytics, Data Science, Data

Photo by Alexandre Debiève on Unsplash

What is AutoML?

The development of a model involves a lot of repetitive and tedious tasks inside the Model Development Life Cycle(MDLC), such as tuning the hyper-parameters, generating and selecting features. These tasks consume a lot of time during the development as they are iterative and various permutations and combinations have to be tried to arrive at the best possible combination of model parameters. Different AutoML tools try to automate different parts of the MDLC process. While some try to specifically only work on automating the development of the model, others try to also automate the feature engineering, while some others might be taking into aspects other steps in the lifecycle.

AutoML tries to achieve its goal by removing such tedious tasks from the hands of the DS which will help the DS and the organization save precious time and money. The approach taken by AutoML tools to achieve this goal, however, varies widely as each of them has different capabilities. While some tools try to automate the entire MDLC, others try to automate certain parts of the Lifecycle.

AutoML can, in fact, be described by a phrase — ‘process of automating the automation of automation’; software development is a process used to leverage computing process and thus reduce human hours required for a task. There are certain tasks that can’t normally be performed by computers as it requires a huge set of rules, Machine Learning automates this process. Now, AutoML tries to remove the redundant and repetitive processes inside the MDLC. This makes the above phrase to describe AutoML, quite apt.

How does AutoML alleviate the complexities?

AutoML tools usually have a Graphical User Interface(GUI) which can help guide users without the need for a technical/programming knowledge. This is commonly called the ‘no-code AI’ approach. However, there are also tools which involve the use of code to work with it.

Why is AutoML required?

Imagine that you have a huge retail company called ABC. ABC has stores throughout the world, of multiple sizes and capacity and is rapidly explained. The following are a few questions it can try to answer using Machine Learning:

Supply Chain Optimization

When to ship a certain product?

What products could create maximum profit?

Which item is most likely to go out of stock in a certain stock at a certain point of time?

Promotion Management

Which type of campaign is the most effective?

Are the campaigns having a combined effect or are they independent of each other?

How much money should be spend on promotions in certain areas?

When should promotions be carried out?

Digital Marketing Management

Which type of marketing will be the most effective?

Will digital marketing campaigns have a significant impact on the area?

Customer Analysis

What type of customers are most likely to visit a store?

The questions listed above are a small fragment of the entire world of applications of Data Science. To implement and maintain these, thousands of Data Science will be required, which, for many companies, might not be feasible. AutoML is hence, a necessary evolution for companies to enhance the productivity of its Data Science divisions.