Democratizing Data-Driven Processes Through AutoML for Better Business Prospects

by May 17, 2020

Data Science and Machine Learning are among the most deployed and useful technologies of the current marketplace. And as the utility increases, the new wave of advancements hit the industry with more innovations in its tides. Similarly, to add an extra edge to what Data Science and ML could achieve, we now have AutoML (Automated Machine Learning) platforms. It is among the top trends of contemporary data-market with most of the big techs investing in its successful incorporation. Companies including Google, Amazon, Microsoft have already embraced AutoML in their business processes to accelerate the effectiveness of their operations and products. Considered as a quiet revolution in AI, the technology has transformed the entire data science landscape while offering a great deal to modern-day businesses.

 

Let’s know what AutoML actually is?

Automated machine learning (AutoML) is the process to automate an end-to-end process of leveraging machine learning algorithms to real-world problems. One of the most peculiar features of the technology is that even people with no data science or ML expertise can work with this platform to carry out desired outcomes.

 

But why do we need AutoML?

According to Gartner’s survey, it takes around 4 years to make an AI project go live which doesn’t cope-up with the rising demand and transforming market dynamics. And, according to statistics, huge investments in data and AI projects are only successful 15% of the time. However, with the rise in current trends and the AutoML platform, small AI projects can be produced in a short period of time.

Moreover, the soaring demands for machine learning systems don’t imply the successful deployment of ML models across a wide range of applications. Its success requires a proficient team of seasoned data scientists and a team that decides which model is the best for a particular business problem. But the shortage of data science talents has doesn’t quite fulfilled the scenario. Here enters the AutoML platform which tends to automate the maximum number of steps in an ML pipeline while reducing the human effort without compromising on the quality of performance.

 

So how is it changing the landscape of modern businesses?

Have you heard of Mercari? Mercari is a popular online shopping app in Japan. The company uses Google’s AutoML tool in order to better process the image classification. Using a UI for uploading photos, Mercari’s app can identify and suggest brand names from over 12 major brands through customized AutoML pipeline technology.

Leveraging Google’s AutoML platform enabled the company to customize ML models in successfully identifying over 50,000 images with an accuracy of 91.3%.

Moreover, the implementation of automated machine learning across physical retail stores is redefining their future with rich business benefits including better sales forecasting and significant others. Analyzing the available current customer data and purchasing season, the AutoML platform can help retail industry businesses with better sales prospects. This can subsequently reduce the unused inventory costs and waste in unnecessary promotions.

While leveraging the AutoML to enhance business effectiveness and productivity, brands can also improve customer personalization through customization.

For any business across any industry, AutoML is bound to make cost reductions and increase productivity for data scientists while the democratization of machine learning reduces demand for them. The technology also helps accelerate revenues and customer satisfaction. AutoML models with enhanced accuracy possess the capability to improve other, less tangible business results too.