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AutoML: the Accurate, Quick and business-savvy Machine Learning

Published on Mar 25, 2020
byChristelle Julias

Quick and accurate business insights => Competitive edge

Quick business insights are the tenets of every modern enterprise, and forever, at the core of sustaining a competitive edge.

While endeavoring to do so, Small and Medium Businesses continuously face the pressure of delivering high performance, while being confronted with the challenge of time-paced technological changes.

This implies the necessity to embed a form of artificial intelligence such as Machine Learning and Deep Learning into their data analysis systems, in order to stay on track with their competitors.

However, nor the budget for the required hardware architecture, nor the skillset for mastering these technologies could be taken for granted. All of those require a lot of investment in terms of computer resources and staff training which simply eliminates those who feel reluctant to do so.

The biggest stake on the table is that trends are ever-evolving and those who cannot afford following them, need to outpace themselves at the risk of being beaten to the seam in this race against time.

AutoML: a friend or an enemy?

Challenges regarding AI implementation are numerous but there are ways to overcome them. There exist some alternatives, some smarter, cheaper and relatively easier ways of ethically cheating in the AI race with the introduction of AutoML.

AI pioneers and giants such as Google, Amazon, and Microsoft inter alia, have already designed their own cloud solution and gained a large base of regular users.

The AutoML technology first received controversial reception among the data scientist community in which tremendous questions raised such as: will it either be “a friend” or a “mere enemy”? Somehow, with time, most found the numerous and undeniable advantages of incorporating AutoML in their toolsets.

Apart from offering a higher degree of automation for ML modeling and for deploying intelligent projects, AutoML or Automatic Machine Learning has been conceived to target the issues of ​ time-consuming​ and obviously ​ costly operations.

AutoML could in some way be a path for achieving quality artificial intelligence automation without the need of hiring expensive staff while providing users with the relief of affordable Artificial Intelligence integrations.

In other words, AutoML democratizes ML i.e renders accessible to the mass, and enable the complex machine learning and deep learning algorithms that used to be reserved for domain experts.

Use cases

AutoML among other uses covers the trickiest parts of ML modeling such as:

  • data preparation,
  • data pre and post-processing
  • feature engineering
  • model selection,
  • selection of evaluation metrics
  • and hyper-parameter parameterizing and optimization.

All can be performed with AutoML in a matter of minutes.

Thanks to the integration of data analysis packages such as Pandas, modeling tools such as Scikit-Learn, TensorFlow and Keras, the borrowing of Jupyter cascading stream of snippets, AutoML promises to offer a complete solution to render ML as intuitive as possible and push the boundaries of any shortcoming of resources and ML skills.

Recommended reading

To learn more about the AutoML, discover in these articles all about this recent business solution and off-the-shelf Machine Learning package of frameworks and services: Freiburg-Hannover

AI Multiple

Towards Data Science: What is AutoMl by Siobhàn K Cronin

The Death of Data Scientists – will AutoML replace them?

3 Reasons Why AutoML Won’t Replace Data Scientists Yet

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