AutoML is becoming an important part of machine learning for the futuristic ML/AL engineers.

Let us assume that business ‘A’ identified a problem and called their Artificial Intelligence or Machine Learning engineers’ team. The team then gets briefed about the situation and were tasked to arrive at a predictive analytics solution.

The traditional workflow involves different team collaborating together to arrive at a solution.

The workflow is complex involving –

This is what traditionally the team can do –

It is a long procedure and takes time.This face of ML-based solutions is changed by Google’s AutoML.

Where, the workflow is minimized as –

This is the basics of AutoML. The steps that take place between data acquisition and prediction is abstracted by the AutoML platform. The businesses can bring their dataset, identify labels, and set the button. The trained and optimized machine learning model predicts. In brief, most of the steps are handled behind the scene providing time and opportunity for businesses to stay focused on problem and solutions rather than process and workflow.

Many of the AutoML platforms support to export the trained model into mobile devices running in iOS and Android, enabling developers to integrate easily within their mobile devices. As they get exported into the Docker container, DevOps team can deploy them at scale and infer in production environments.

So, AutoML is promising for the non-tech companies to build ML applications and access the capabilities at lower costs. Isn’t it?

Google launched its Cloud AutoML in 2018. It has its proprietary algorithm. AutoML enables businesses to benefit from data-driven applications powered by statistical models. It can automate many of the tasks performed by data scientists.

Google uses premium Transfer Learning and Neural Architecture Search technology to conduct automation in machine learning through cloud.

A few of the libraries used for automating machine learning are as listed below.

• Eclipse Arbiter is a hyperparameter optimization library that automates hyperparameter tuning for deep neural net training.
• Featuretools automates engineering features from relational and transactional data
• Auto-sklearn is a replacement for scikit-learn estimators
• MLBox to support model stacking
• TPOT to find the ML pipelines that are best performing

Other libraries include Xcessive, Advisor, Hyperpot, Spearmint, RoBo, BayesianOptimization, Optunity, ATM, and HyberBand.

Let us understand, how Google’s AutoML is beneficial for the businesses while predicting solutions.

Benefits of using Google’s AutoML

A few of them are briefed here. They include –
• Runs repetitive tasks automatically with improved efficiency.
• Facilitates data scientists to focus on problems rather than models.
• Avoids potential manual errors.
• Allows everyone to use machine learning features.
• Reduces time to implement machine learning process.
• Organizations can build production-ready models quickly.
• Improved scalability as the model can get deployed for different use cases.

Moving forward, let us see a couple of real-world instances for AutoML.

1) Google AutoML to detect Pneumonia with chest X-ray images

Google Cloud AutoML Vision simplifies the creation of custom vision models to recognize images. It is used to develop medical image classification model that can detect pneumonia using chest X-ray images.

2) Restaurant location recognizing model

AutoML is used to identify the restaurant by looking at the image of the noodle bowl. The model is able to analyze minute details of the image and predict which restaurant it was made in.

There are several real-world examples that demonstrates the capabilities of AutoML. So, AutoML seems to be a promising solution for companies to bridge the talent gap in the data science industry.

It is an important part of machine learning for AL/ML engineers as AutoML happens to be the future of Artificial Intelligence.