Automated machine learning (AutoML) is a process that automates some of the more complex or benign steps of the machine-learning lifecycle. This helps those without a theoretical background or practical experience with machine learning participate in AI development.
AutoML helps users transfer data to training algorithms and automatically search for the best neural network architecture for a given issue. This saves data science practitioners a huge amount of time. Often, tasks that would take hours to complete can be accomplished in minutes using AutoML.
AutoML helps deomcratize machine learning by allowing non-trained users to use machine-learning tools and technologies. AutoML tools help bridge the talent shortage, allowing companies to scale their AI implementations.
Before AutoML, data scientists were forced to perform manual, tedious operations with their data. Those tedious tasks often resulted in human-caused mistakes. AutoML allowed data scientists to reduce or eliminate the repetitive, manual tasks that absorbed much of their time.
The demand for expert-level knowledge in machine learning is outpacing supply. This is manifesting itself through open positions that far exceed the number of qualified applicants. AutoML aims to narrow this gap by automating processes that would otherwise be too complex for anyone other than a field expert.
This automation has led to user-friendly machine learning software with simple interfaces that anyone with beginner technical knowledge and time to learn the toolset can use, enabling non-data-science analysts, marketers, and IT staff to implement machine learning into their workflows. By scaling machine learning across various industries, all organizations benefit from boosted efficiency and effectiveness in the fields that need it most.
AutoML is a helpful tool for novice and advanced AI practitioners. H2O’s AutoML provides a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code. This benefits users in a variety of fields such as:
Financial Services - Traditional financial companies and newer fintech use AutoML technologies to overcome challenges in their industry such as AML, transaction fraud, credit risk lending failures, trade failures and customer churn.
Government - Government agencies are turning to AI and AutoML to optimize their large data stores to improve fraud, waste and abuse, predictive maintenance across agencies and communities, supply chain and logistics, internal and external cyber security, and human resources.
Health - AutoML provides private and public healthcare professionals optimized data and best practices for hospital operations, clinical application, life sciences and biopharma, precision medicine, supply chain and transportation, marketing, human resources, and finance.
Insurance - The Insurance industry is using AutoML to fill knowledge gaps and optimize claims management, precision pricing, automated underwriting, customer churn, and fraud prevention.
Manufacturing - Leading manufacturers use AI and machine learning to reduce costs and streamline operations through demand forecasting, predicting stock levels, predictive machine maintenance, returns forecasting, and fault detection within their supply chains.
Marketing - Marketing agencies and organizations use AutoML to produce market predictions, optimal ad placement, investment opportunities, create targeted lead generation, upsell and cross-sell promotions, implement funnel predictions, and create customer segmentation and recommendations.
H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.
H2O offers a number of model explainability methods that apply to AutoML objects (groups of models), as well as individual models (e.g. leader model). Explanations can be generated automatically with a single function call, providing a simple interface to exploring and explaining the AutoML models.
AutoML is designed to automate the individual steps in the machine learning process, whereas artificial intelligence software is designed to simply simulate human thinking.
There is a public assumption that AutoML will, at some point, replace the field of data science and as such, organizations should invest resources into one over the other. The “AutoML vs Data Science” mentality is inherently flawed. While AutoML can leverage pieces of the machine learning pipeline without an expert data scientist, that does not invalidate the field data science itself. In fact, AutoML more frequently acts as an accelerator to data science by automating its repetitive aspects. AutoML enables data scientists to dedicate more of their time solving expert-level technical issues.
Data mining reviews patterns in existing data, and AutoML uses those patterns to make predictions.
AutoML focuses on predictions, while statistics focuses on sample, population, and hypotheses.