Satyakam Mohanty

Automated Machine Learning or AutoML has emerged as an exciting new branch of AI/ Machine Learning. Existing Machine Learning algorithms require a certain amount of pre-processing of the dataset to make it suitable for machine learning and this requires considerable expertise and knowledge of ML. AutoML helps automate this process. In essence, AutoML is the process of automating automation. Not only does AutoML help expedite the process of creating AI solutions, it also helps create superior solutions.

The emergence of AutoML can certainly situate itself in the life cycle of growing ML products and help bump up their demand by leaps and bounds. Here are four reasons why:

  1. Addresses the ML Knowledge Gap

Even though most businesses now understand the potential that ML adoption can bring, they find it challenging to deploy it because it requires extensive investment in the form of highly paid data scientists. Given that the number of experienced data scientists is extremely small, it isn’t always possible or feasible for businesses to find them. AutoML helps bypass this need to a large extent.

  • Helps Data Scientists Level Up

By helping automate some of the highly time-consuming manual processes, AutoML can free up considerable time from data scientists, thereby enabling them to solve more high-level problems rather than getting stuck with the implementation of existing ones.

  • Eliminating Manual Bias

One persistent problem with AI has been the number of obvious and non-obvious biases that often creep into models due to the reliance on existing data sets as well as the prevalent human bias. AutoML can help mitigate this to a large extent given that there is little impact of human bias on models created using AutoML.

  • Democratization of ML

AutoML can play a huge role in enabling the democratization of AI, making it accessible to a large variety of businesses, rather than being the exclusive preserve of a select few.

What’s happening with AutoML?

There have been some pretty interesting case studies on the use of AutoML by some innovative organizations. For instance, AirBnB is using AutoML in some useful ways such as benchmarking, diagnostics and exploration. This is possible because AutoML builds candidate models much faster. AirBnB also uses AutoML for applications such as ensuring unbiased presentation of challenger models to help the data scientist choose the best model family. This is because AutoML can present a plethora of challenger models using the same training set as the incumbent model.

Even Google has some fascinating use cases relating to the use of AutoML. For example, Google released a commercial version of AutoML in 2018, which helps create custom image-recognition software. Today, the most accurate results achieved on a standard benchmark for visual AI software, ImageNet, were achieved by neural networks designed by neural networks, not humans. 

By all accounts, AutoML is slated to define the future of machine learning. AutoML is still an emerging field and not a lot of organizations are fully realizing its benefits. As a result, there is a huge opportunity to venture in this space and steer ahead of competition.  AutoML will definitely make it far more conducive for businesses to adopt AI.

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