https://cloud.google.com/automl-tables/
AutoML Tables enables your entire team of data scientists, analysts, and developers to automatically build and deploy state-of-the-art machine learning models on structured data at massively increased speed and scale. Transform your business by leveraging your enterprise data to tackle mission-critical tasks like supply chain management, fraud detection, lead conversion optimization, and increasing customer lifetime value.
Produce state-of-the art models (see features for more details) with one click. We automatically search through Google’s model zoo for structured data to find the best model for your needs, ranging from linear/logistic regression models for simpler datasets to advanced deep, ensemble, and architecture-search methods for larger, more complex ones.
AutoML Tables automates feature engineering on a wide range of tabular data primitives — such as numbers, classes, strings, timestamps, and lists — and also helps you detect and take care of missing values, outliers, and other common data issues.
Our codeless interface guides users through the full end-to-end machine learning lifecycle, making it easy for anyone on your team to build models and reliably incorporate them into broader applications. We provide extensive input data and model behavior explainability features, along with guardrails to prevent common mistakes. AutoML Tables is also available in API and notebook environments.
AutoML Tables uses Google’s low-latency serving infrastructure, which makes deploying machine learning models extremely easy, regardless of production workload volume and global reach.
AutoML Tables reduces the time it takes to go from raw data to top-quality, production-ready machine learning models from months to just a few days.
AutoML Tables doesn’t require a large annual licensing fee. It’s priced based on compute and memory usage, so you’ll only get charged for what you actually use.
See how you can use AutoML Tables to turn your structured data into predictive insights
The speed, precision, and scale of AutoML Tables allowed Fox Sports to create an entirely new experience for millions of cricket fans across Australia. By training our model on the multiple variables of historical cricket matches we could predict when wickets would fall 5 minutes before it happened on the pitch. This new feature became a fundamental part of our marketing strategy through integrating AutoML with App Engine and Cloud Dataflow to transform every customer touchpoint. We’ve showcased cricket like never before with user engagement up 140% vs industry averages and the marketing for this activity delivering 150% more subscribers per dollar spent by communicating to fans in the right place at the right time. Chris Pocock, Marketing Director, Fox Sports
AutoML Tables lets us anticipate how players will behave in our Harry Potter game. With over a million unique users, within three days after the app is installed, we can begin to predict their game behavior. Accordingly, we can use Google AdWords to target the players we think will become paying users on our platform, for more efficient ad spend and higher revenue generation. Joshua Clark, Lead Data Scientist, Jam City
AutoML Tables has allowed Indiana University to experiment with our existing work, which has led to some impressive results. The accuracy of out-of-sample predictions from this turn-key solution were strong, considering the lack of structure on the models. AutoML quickly provided analysis and insights to data that would have taken weeks to otherwise implement. The intuitive web interface and the direct integration with BigQuery rapidly accelerated the training and deployment of predictive models at institutional scale. IU plans to continue to leverage this service for future analytics initiatives to increase speed and scale of IU projects. Ben Motz, Faculty Fellow for Academic Analytics, University Information Technology Services, Indiana University
Using Google AutoML Tables, we extended models within our international order gateway, serving hundreds of our retail clients, to significantly improve fraud detection. Our engineers were able to develop and deploy highly accurate models within two weeks, an effort that would have typically taken months. With Google AutoML Tables, we are looking forward to democratizing AI by providing access to this technology not only to our data scientists, but also to teams of engineers globally. Olga Lagunova, Chief Data and Analytics Officer, Pitney Bowes
AutoML Tables has allowed us to simplify and speed up the creation of ML models that will help us solve relevant business problems. Using the technology, we expect to increase the automatic underwriting of medical insurance from 55% to 80% and to reduce healthcare expenses by an additional 10% due to more accurate fraudulent claim detection. The interface is so easy to use that most of our team can now create custom models, which also mitigates the scarcity of highly-trained data scientist Enrique Ibarra, CIO, GNP Seguros
As a Brazilian web-first company offering monthly subscription-based insurance policies for automotive, home, and life, it’s important to optimize our marketing investments to better target customers. With AutoML Tables, we were able to predict customer lifetime values of car insurance policies at various stages in the funnel, which is helping us reduce acquisition costs and increase retention. The user interface was easy for our team to pick up, and the resulting models were often more accurate than our custom ML work. Thais Presutto Business Intelligence Manager, Youse