Prevision.io has built and launched its new AI management and MLOps platform, a pay-as-you-go and AI-dedicated solution built on Google Cloud.
Also known as Prevision.io, the new AI management platform is designed to simplify the machine learning project lifecycle while offering analytics capabilities. Considered as the first-of-its-kind and available exclusively on Google Cloud Marketplace, Google Cloud users are now expected to have the ability to experiment, build, deploy, and manage AI projects in the cloud within a shorter time without having extensive data science knowledge.
“My team of data scientists saw a real need for software that could democratize machine learning innovation by removing these common barriers. We knew that features like automation could make building and deploying AI much more doable for other data scientists, as well as citizen data scientists. So, we launched Prevision.io,” said Tuncay Isik, CEO of Prevision.io.
“Regardless of industry or department, we’ve seen Prevision.io help businesses solve some of their biggest challenges. Utilities companies are relying on better forecasts of the energy consumption (gas or electricity). Transportation companies have deployed machine learning models that can inform logistical operations based on fluctuating supply and demand.”
As described, Prevision.io can be connected to BigQuery or other datastores on Google Cloud for the launch of machine learning projects which can also be managed across the entire lifecycle through this AI management platform.
Other capabilities of Prevision.io include enabling users to experiment with iteration and optimization to get an effective model into production from the start. Also, users can automate training and prediction tasks to improve collaboration, reduce time-consuming manual operations, and boost results. Users can further implement automation across the production pipeline with built-in features like AutoML and a scheduler for recurring tasks.
With Prevision.io, deployments can be tailored using REST APIs or as a component to generate batch predictions, as well as monitor infrastructure and model behavior to understand resource utilization and how data changes over time.