Automated machine learning (AutoML) is regarded as a “quiet revolution” in AI. But what makes AutoML revolutionary is not just automation and acceleration of the machine learning process, but also its support for increased accuracy of machine learning models and its democratization of data science. AutoML is definitely in a hype cycle, but it’s also an effective technology that we believe will be integral to a new era in AI.
AutoML has two main goals. The first is to democratize data science so that internally complex models can be utilized by everyone to improve decision-making. To the extent AutoML succeeds here, it can solve the problem of not having enough data scientists to make sense of big data and give everyone a chance to be part of every step of decision-making. The second goal is to automate many repetitive, tedious tasks involved in machine learning. For example, with a manual process, it can take a very long time to try a range of machine learning algorithms in order to find the best one. AutoML automates such tasks, and trains models in parallel, thus maximizing the optimization and increasing the overall accuracy of models an organization may build.
AutoML’s value comes from automating multiple machine learning steps, such as exploratory data analysis, data preparation, feature engineering, model selection, model training, hyperparameter tuning, and deployment. Automating all these steps shortens the traditional data science workflow significantly because users need only upload their data, set a target variable (such as the column to be predicted) and, in most cases, let the AutoML engine do the rest. As such, AutoML not only enables more people to take advantage of machine learning, it also helps experienced data scientists focus on more important strategic steps, like conceptualizing models and carefully reviewing their performance in order to avoid bias. Last but not least, AutoML helps to prevent the human errors that can occur when model optimization is carried out manually.
AutoML has become pervasive across different industries and use cases and the technology is offered in a wide variety of solutions from open source tools to commercial products. Understanding what AutoML is about, where the current breakthroughs are happening, and who the major vendors in the space are is essential for having meaningful discussion and astute planning around leveraging AutoML in your organization. This GigaOm Radar report will help enterprise buyers become familiar with AutoML and track the AutoML state of the art.
Among the key findings of the report:
- Feature engineering is a critical capability and machine learning model explainability is a key differentiating factor for AutoML platforms.
- Integration of operational and production AI capabilities is growing.
- AutoML still has room for growth regarding integrating domain knowledge into machine learning; therefore, it cannot replace data scientists. Auto ML can’t point out a business problem, nor can it specify how its predictions might help solve a business problem—at least, not yet.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.