A360 Operate provides scalable, secure Kubernetes cluster environments for executing Project Workspaces (Jupyter Notebooks) and serving machine learning models as REST APIs for scoring and performance metrics. Clusters are backed by cloud-native AI compute capacity such as GPUs.
Are you prepared for
Model Scalability?
“Wait a minute, how much traffic should the model hosting server support?” – towardsdatascience.com
Unknown usage patterns and requirements lead to instability and failure to realize AI value. Having an automated scalable and secure environment for model serving that eliminates manual management greatly improves the return on investment.
Why Businesses Use A360 Operate to Serve Models
Automated Infrastructure
Eliminate the hassle of developing and maintaining the deployment of infrastructure and Kubernetes clusters to serve AI Models when A360 Operate can do it for you in a fully automated way.
Multi-Cloud Support
By standardizing on Kubernetes cluster environments for model serving, A360 Operate can be deployed to any cloud environment including on premises.
Flexible Compute Options
Kubernetes clusters are configured with multiple node pools of varying compute and GPU capabilities so that your AI models are matched to the optimal environment for serving.
Secure-First Strategy
Bring your own base container images to ensure your security compliances are met while REST APIs are exposed only with proper authentication and authorization.
Integrates with GitOps
Using A360 Starpack technology, your models are deployed and updated automatically in A360 Operate following your GitOps processes.
Monitoring Built-In
Model containers running in A360 Operate are automatically connected to A360 Monitor for model performance management.
Optimize Cost
Streamline your model runtime cost by optimizing your model operations using A360 AI. A360 provides autoscaling that can help unlock cost savings by automatically scaling up and down the resources in use to better align capacity to demand.