In recent decades, technologies like artificial intelligence (AI) and machine learning (ML) have gained significant adoption into various business domains. Machine learning, in particular, has transformed business domains like healthcare, financial services, and retail services.
Machine learning technology is no longer just for technology geeks but is now understood and used by business users. However, future growth of this technology continues to be dependent on the availability of skilled ML workers and data science experts. With the current shortage of skilled ML professionals, most businesses do not have the budget nor the resources to invest into trained team proficient in these technologies.
The answer lies in the emergence of the automated machine learning pipeline technology also known as AutoML pipeline.
Through this article, we shall evaluate the business value of customized AutoML pipeline solutions and how this technology can be built for any business.
What is AutoML Pipeline?
Short for automated machine learning pipeline, AutoML pipeline is an automated process of applying the machine learning technology to real-world business problems. In other words, it automates the entire pipeline right from obtaining the dataset (from diverse data sources) to the development of a customized machine learning model for the business.
How does AutoML technology benefit businesses? In real sense, this solution enables the use of machine learning even for non-technical users with no knowledge of the underlying technology.
In a “traditional” machine learning model, human intervention and expertise is required at multiple stages including data ingestion, data pre-processing, and prediction models. On the other hand, using AutoML, each step other than data collection and prediction can be automated to create a customized AutoML pipeline for any business customer.
Next, what is the need for creating a customized automated machine learning pipeline? The growing demand for machine learning models by business enterprises is driving the development of user-friendly ML systems that can be used off the shelf by any business user.
Through its automation, a customized AutoML pipeline can provide the following benefits:
- Improve productivity of data experts by automating any repetitive ML-related tasks and help them focus on other issues.
- Reduce human errors in ML models that arise mainly due to manual steps.
- Make machine learning accessible for all users, thus promoting a decentralized process.
In the next section, we shall discuss some of the popular frameworks in AutoML (or AutoML tools) that are enabling a production machine learning pipeline.
AutoML Pipeline – Frameworks and Processes
Automated machine learning pipeline tools or frameworks can be used to automate the complete (or partial) ML pipeline. Here are few of the more popular frameworks being used:
Built using a Python library, MLBox is an AutoML tool that can automate ML-related tasks such as data pre-processing, optimizing (for cross-validating the ML models), and for making predictions. Some of its main features include the latest predictive models using deep learning, optimization of hyperparameters, and faster data pre-processing and cleaning.
Integrated on top of the scikit-learn machine learning package in Python, Auto-Sklearn is the automated version that frees any ML user from tasks such as selecting the right ML algorithm and tuning the hyperparameters. Suited for small to medium datasets, Auto-Sklearn can create and optimize a machine learning pipeline using Bayesian searches.
Launched by Google, Cloud AutoML is a suite of machine learning-related products that is targeted for developers or users with limited knowledge about ML models. With a very user-friendly GUI, Cloud AutoML uses built-in functionalities like neural architecture search and transfer learning to design and deploy ML models based on the latest data.
Evaluating the AutoML Pipeline Architecture
The AutoML pipeline architecture can make use of automated ML algorithms that can replace the ML engineer with a set of algorithms for automatic tuning of the ML model. For instance, the Cloud AutoML uses the neural architecture search (or NAS) algorithms that can speed up the development of any deep learning applications without evaluating many architectures.
AutoKeras is one such ML framework and library that features NAS and hyperparameter optimization for implementing an automated machine learning pipeline architecture. Through hyper-parameter optimization and automated algorithms, this architecture provides methods that can automate the manual and time-consuming process of hyperparameter tuning for new tasks.
Finally, we shall look at the benefits and use cases of AutoML pipeline in business operations.
AutoML Pipeline – Business Benefits and Use Cases
Among the many use cases of AutoML pipeline, Mercari, a popular online shopping app in Japan, uses the Google AutoML tool for the purpose of image classification. Through a UI for uploading photos, the shopping app can identify and suggest brand names from over 12 major brands with its customized AutoML pipeline technology. Their customized ML model was successful in identifying over 50,000 images (with an accuracy of 91.3%).
The future of AutoML technology is also driving physical retail stores that are looking towards machine learning to leverage their vast customer data. Automated ML promises rich business benefits for the retail industry, including:
- Better sales forecasting on the basis of current customer data and purchasing season. Automated ML enable retail brands to identify and stock the in-demand products while assuring product availability for the consumers.
Accurate forecasting also reduces unused inventory costs and waste in unnecessary promotions.
- Improved customer personalization that is based on their previous purchases and their future purchases through customized ML algorithms. For physical retail stores, this means the convergence of offline and online technologies.
How does the emergence of automated machine learning technology impact the work of data scientists and other analysts? Does it make them redundant?
AutoML promises to enhance the productivity of data scientists or machine learning experts by automating the development of ML models and algorithms. Developing new ML models and algorithms form a small part of their functions. As a result, automation frees up data scientists to focus on solving business-related problems through machine learning solutions.
The future growth of automated machine learning is driven by the trend that it is becoming accessible to every technology user. With AutoML, data scientists can accelerate the “mainstreaming” of machine learning in the business process for maximum business benefit.
As a result, AutoML is being billed as the future of machine learning technologies.
Through the automation of machine learning tasks like pipeline creation and hyperparameter tuning, AutoML is enabling data scientists to improve their productivity and realize their true potential. Through this article, we have evaluated some of the popular AutoML frameworks and tools.
With its expertise and industry experience in deep learning technologies like AI and machine learning, Countants provides its clients with the right technology platform for business success and growth. Through its customized solutions, this analytics company has made data insights and business intelligence more accessible and beneficial to any business user in any industry domain.
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