Whether you want to integrate an AI-powered industry to solve a particular business problem or you have a team of ML engineers and data scientists looking for tools and frameworks to scale their work, Google claims to have the breadth and depth in AI and machine learning to fit your needs.
To improve the MLOps experience, Google is pre-announcing: Prediction backend GA, Managed Pipelines, Metadata, Experiments, and Model Evaluation. These features provide automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.
Further, Cloud AI Building Blocks provide access to commonly used models via APIs. By the end of September, Google plans to include AutoML in the AI platform as an integrated function in the workflow.
The company said it is also steadily transfering advancements from Google AI research into cloud solutions to help you create better experiences for customers.
One such area is contact centers. Contact Center AI (CCAI) is designed to speed-up customer requests using virtual agents, help assist live agents, and offer insights on all your contact center data to improve customer interactions.
Google has also introduced Lending DocAI, a new, specialized solution powered by Document AI for the mortgage industry. The solution processes borrowers’ income and asset documents to speed-up loan applications.