cnvrg.io Releases New Streaming Endpoints With One-click Deployment for Real-time Machine Learning Applications

cnvrg.io , Endpoints, Real-time Machine Learning Applications
cnvrg.io Releases New Streaming Endpoints With One-click Deployment for Real-time Machine Learning Applications

cnvrg.io, the data science platform simplifying model management and introducing advanced MLOps to the industry, announced its streaming endpoints solution, a new capability for deploying ML models to production with Apache Kafka in one click. cnvrg.io is the first ML platform to enable one-click streaming endpoint deployment for large-scale and real-time predictions with high throughput and low latency.

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85% of machine learning models don’t get to production due to the technical complexity of deploying the model in the right environment and architecture. Models can be deployed in a variety of different ways. Batch deployment for offline inference and web service for more real-time scenarios. These two approaches cover most of the ML use cases, but they both fall short in an enterprise setting when you need to scale and stream millions of predictions in real-time. Enterprises require fast, scalable predictions to execute critical and time-sensitive business decisions.

cnvrg.io is thrilled to announce its new capability of deploying ML models to production with a streaming architecture of producer/consumer interface with native integration to Apache Kafka and AWS Kinesis. In just one click, data scientists and engineers can deploy any kind of model as an endpoint that can receive data as stream and output predictions as streams.

Deployed models will be tracked with advanced model management and model monitoring solutions including alerts, retraining, A/B testing and canary rollout, autoscaling, and more.

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This new capability allows engineers to support and predict millions of samples in a real-time environment. This architecture is ideal for time-sensitive or event-based predictions, recommender systems, and large-scale applications that require high throughput, low latency, and fault-tolerant environments.

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