AutoML: The Next Wave of Machine Learning

Automated Machine Learning: AutoML

Courtesy Heartbeat

Machine learning has provided some significant breakthroughs in diverse fields in recent years. Areas like financial services, healthcare, retail, transportation, and more have been using machine learning systems in one way or another, and the results have been promising. Automated Machine Learning (AutoML) automates the maximum number of steps in an ML pipeline—with a minimum amount of human effort and without compromising the model’s performance.

Mercari is a popular shopping app in Japan that has been using AutoML Vision (Google’s AutoML solution) for classifying images.

While their own model—trained on TensorFlow—achieved an accuracy of 75%, AutoML Vision in advanced mode with 50,000 training images achieved an accuracy of 91.3%, which is a whopping 15% increase. With such astounding results, Mercari has integrated AutoML into their systems.

This is just one example of how AutoML is fundamentally changing the face of ML-based solutions today by enabling people from diverse backgrounds to evolve machine learning models to address complex scenarios.

The Need for AutoML

Machine learning today is not limited to R&D applications but has made its foray into the enterprise domain. However, the traditional ML process is human-dependent, and not all businesses have the resources to invest in an experienced data science team. AutoML may be the answer to such situations.

The success of machine learning in a wide range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts¹. AutoML tends to automate the maximum number of steps in an ML pipeline—with a minimum amount of human effort and without compromising the model’s performance.

Advantages

The advantages of AutoML can be summed up in three major points:

  • Increases productivity by automating repetitive tasks. This enables a data scientist to focus more on the problem rather than the models.
  • Automating the ML pipeline also helps to avoid errors that might creep in manually.
  • Ultimately, AutoML is a step towards democratizing machine learningby making the power of ML accessible to everybody.