What is AutoML?
Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems.
In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. - From Wikipedia
Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand.
1.Open Source
AI is one of the few scientific areas were despite significant corporate investment, even secretive tech giants like Apple publish their research findings. Therefore it should not be surprising that there are competitive open source autoML tools.
All open source tools we came across, need an active development environment in Python or R and require the user to write at least a few lines of code to initiate the automated machine learning process.
For example, Auto-Sklearn, Caret, TPOT etc.
2.Startups
Given the scarcity of AI talent, AutoML can have a dramatic impact on democratizing AI and making companies more effective and efficient. Numerous startups are being launched and raise ambitious amounts to capture this opportunity.
Most startups aim to offer tools that can be launched with a clean user interface by a non-technical user. Some tools also offer visualization to explain their findings and the resulting model.
For example, H20.ai, DataRobot, PurePredictive etc.
3.Tech giants
Google claims to be an “AI-first” company and is delivering on that promise. Google Cloud AutoML is one of the first autoML tools launched by tech giants. IBM’s SPSS is one of the most popular analytics software providers and has been offering for various autoML tools such as auto classifier.
To close the gap where in this world only have a handful of companies have access to AI and ML, and to make AI accessible to every business, startup or tech giants are introducing AutoML.
Take Google as an example, Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google. It believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of.
Increased accuracy: AutoML Vision is built on leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.
Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.
Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high quality model customized for your specific needs.
Mike White, CTO and SVP, for Disney Consumer Products and Interactive Media, says to Google Cloud AutoML:
“Cloud AutoML’s technology is helping us build vision models to annotate our products with Disney characters, product categories and colors. These annotations are being integrated into our search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery and product recommendations on shopDisney.”
First created and edited by AI Analytics;
Source: Google Cloud AutoML; Wikipedia etc.