A new feature of Google’s Vertex AI platform, Private Endpoints, promises to improve privacy and reduce latency for online prediction tasks by eliminating the need for data to go through any public networks before making it back into VPCs.
Vertex AI, released by Google at I/O 2021, is a fully managed machine learning platform that makes it easy to deploy and maintain large-scale models. The application provides a consistent UI for users to log onto any of the cloud services offered on GCP (Google Cloud Platform) to remove all hassle from the development process. Furthermore, Vertex AI reduces the problem with broken tools used widely across industries resulting in an enhanced customer experience.
Google mentioned that real-time machine learning model prediction is a challenge for companies. It’s important in various industries why Google launched the Vertex AI private endpoint to provide low latency network connections as a solution. With VPC Network, users can connect to internal IP addresses across two networks. Whether the IP address belongs to the same project or organization, all traffic stays in the Google network.
Vertex AI provides machine learning model hosting and prediction services to its users. Users can deploy models in the cloud using an API, allowing them low-latency predictions when online through REST calls. Vertex offers a variety of different parameters for endpoints, including platform type and scaling options, so that accuracy is never compromised due to connectivity or other variables beyond user control.
Private Endpoints are significant because they give you the ability to deploy models without crossing over the public internet. This reduces latency by not having your data wait in line, and it also provides a secure way of deploying what would typically be information such as personal or business-related details.