ParallelM Solves Real-World Machine-Learning Deployment Challenge with Kubernetes Autoscaling REST Endpoint
Summary
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. 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. Using 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. 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. ParallelM’s approach is that of a single, unified MLOps solution that embeds best practice processes in technology, enabling all ML stakeholders to unlock the business value of AI.
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