Real-Time ML Predictions with Google's Vertex AI One of the bi | Big Data Science

Real-Time ML Predictions with Google's Vertex AI
One of the biggest challenges in serving ML-models is providing near real-time predictions. Some business scenarios are especially sensitive to time latency. For example, recommendation systems for online store users, estimating the delivery time of products for food tech companies, etc. On August 25, 2021, Google announced the possibility of direct interaction with Vertex AI - its unified ML platform through private endpoints. Vertex AI allows you to quickly connect a trained and tested ML model to a working application, upload it to a specially prepared server in the Google Cloud, or export it to the desired format.
Vertex Predictions is a serverless way of serving ML models that can be linked in the cloud and made predictions via a REST API. With online forecasts, it is necessary to obtain a model at the endpoint, which will link it to physical computing resources and allow it to be done in almost real time. With VPC Peering, you can configure a private connection to reach an endpoint. By doing this, user data will not pass through the public Internet, which reduces the latency of online predictions and improves security.
https://cloud.google.com/blog/products/ai-machine-learning/creating-a-private-endpoint-on-vertex-ai