ParallelM Addresses Machine-Learning Deployment Challenge

ParallelM, a provider of machine learning operationalization (MLOps) software, has released a new version of MCenter that includes REST-based serving using Kubernetes to create a no-code, autoscaling infrastructure for model serving supporting the leading modeling frameworks.

The MCenter solution is built specifically to power the deployment, management, and governance of machine learning pipelines in production so that companies can scale machine learning across their business applications.

With this release, data scientists can quickly create robust autoscaling REST services for their machine learning models to better serve real-time applications in the cloud or on-premise.

The 1.3 release of MCenter specifically addresses the deployment challenges of machine learning for real-time, production applications. According to ParallelM, many existing data science tools with REST interfaces were designed for testing of model outputs and not for production applications. This means that while these REST endpoints are easy to set up they cannot perform in real-world environments and will fail when they are needed most.

The new REST interface in MCenter is intended for real-time serving models with low latency at high volume as is required by real-world applications. By using this more robust REST endpoint, data scientists can be assured that their models will be available to serve their business applications even under the most punishing real-world conditions.

The new release increases the scalability and performance of ParallelM MCenter across both batch and real-time use cases by using Kubernetes to provide autoscaling infrastructureUsing this industry standard approach allows loads on the infrastructure to scale up and down as needed to optimize resource utilization and manage costs for pay-as-you-go services. Kubernetes also provides robust failover and ease of infrastructure monitoring and management. So, no matter if companies are just starting with machine learning or are already building advanced, real-time AI applications, their platform for ML in production can scale to meet their needs.

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