2020, The Year of AutoML?

AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, will likely become the most popular trend for the year 2020. With the advent of big data, advanced analytics, and predictive models, data scientists today are expected to possess more talent and updated skills when it comes to handling artificial intelligence and machine learning. But these highly skilled data scientists are rare to find. However, bridging the skills gap, the other side of the herd has not only been able to survive but are also capable of building models using the best diagnostic and predictive analytics tools, and part of the reason is AutoML.

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AutoML packages like auto-sklearn can automatically do the model selection, scoring, and hyperparameter optimisation. Services like Amazon Forecast and Google’s Cloud AutoML also help in determining the algorithm to fit best with the data.

According to a report, the data explosion in the world is going to increase tenfold, so the world of analytics, AI, machine learning and data science will see a wave of data and training. And, with the increasing amount of data, here’s why AutoML might be the most used technology in 2020.

Reducing Time To Implement ML Process

The time taken to build an ML model by humans is often too much, and the accuracy is not at par. It would typically take less time for AutoML to implement an ML process when compared to the one under human supervision. With the increasing need for more insights from the big data, organisations are shifting towards amplifying their predictive power by leveraging the abilities of complex automated machine learning.

An ML process typically consists of data pre-processing, feature selection, feature extraction, feature engineering, algorithm selection, and hyperparameter tuning. These take up more time to implement and require considerable expertise; AutoML, on the other hand, removes the trouble of going through some of these tedious processes.

Now, when it comes to big data and analytics, the industry is rapidly increasing, especially regarding the volume and complexity of big data, cloud computing and IoT based services. According to a survey, in 2019, the number of firms investing in big data and AI has ballooned to 33.9% from 27% in 2018. This shows that big data-based technologies and analytics will only be increasing, and that is why AutoML will be one of the prime focus of organisations in 2020 to process the vast data.

Bridging The Skills Gap

AutoML holds the great promise of helping the non-tech companies or companies with less data science expertise with the capabilities of building their ML applications. With the launch of Cloud AutoML, based on Neural Architecture Search (NAS) and transfer learning, Google believes that it has the potential to make the existing AI/ML experts more productive along with helping the less skilled engineered to build a powerful AI system.

Technologies like AutoML have given organisations today the capability to quickly build production-ready models without the help of expensive data science. AutoML uses ML, AI and deep learning to provide businesses, across the world, the opportunity to take advantage of data-driven applications powered by statistical models even with the existing talent gap in the data science industry.

AutoML, along with bridging the talent gap, is also at the same time democratised machine learning. This has helped to carry out processes like hyperparameter tuning, selection of algorithms, and finding the appropriate model — as these tasks are tedious and at the same time complex. Because of AutoML machine learning can now be adapted in various sectors easily by data scientists without any complexity.

Improving Scalability

Generally, when we see machine learning applications like image colourisation, automatic translation, we know that such tasks require massive amounts of data. With this enormous amount of data, training a model takes a long time, and sometimes the model is big and cannot be fitted into a working memory of the training device, and therefore becomes a difficult task.

Plus, the evaluation, experimentation, and deployment of the models might have different use cases. AutoML, on the other hand, makes it easy to handle data, train model, evaluate, experiment, and even deploy the model for different use cases as it takes on the task to find the best algorithm for the task to be done.

Conclusion

Globally the demand for data scientists was projected to exceed supply by more than 50% in 2019. A lot of companies believe that hiring talented data scientists is a tough job because they are scarce and expensive. AutoML is a solution for companies to find a way to bridge the talent gap that exists in the data science industry. Not only does it benefit the less skilled data scientists, but it also saves time for the highly skilled once, so that they can oversee other high priority projects instead of wasting time on the tasks which can be automated by AutoML.

This article was published on analyticsindiamag

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