ParallelM, the leader in MLOps, announced a partnership with Cloudera to add options for bringing machine learning (ML) models from Cloudera ML development environments, including Cloudera Data Science Workbench (CDSW) and the upcoming cloud-native Cloudera Machine Learning platform, into production using ParallelM’s MCenter. MCenter allows for centralized management of ML models taking full advantage of existing infrastructure investments so Cloudera customers can start deploying AI applications at scale.
“Automating the deployment and management of machine learning models in production is a key part of our strategy to industrialize AI,” said Hilary Mason, General Manager, Machine Learning, Cloudera. “Our customers are asking for full model lifecycle management including advanced model health monitoring, and our partnership with ParallelM delivers that and more while leveraging our customers’ investments in Cloudera to help build the environment for the enterprise AI factories of the future.”
The first step in the integration between CDSW, and later, Cloudera Machine Learning, with MCenter is a new model deployment connector. With the new connector, CDSW models are now easily imported in MCenter where they immediately enter an automated production process where they can be managed on any cloud or on-premise. MCenter provides full capabilities for batch, real-time and streaming deployments with integrated model health monitoring and full model governance to track every detail regarding the model’s use in production. Also, CDSW models deployed in MCenter can also leverage the CDH infrastructure for processing including dynamic retraining for streaming or batch predictions. In addition, with production statistics from MCenter, CDSW users can further optimize model updates and new model development using actual production stats, thereby creating a seamless, continuous model lifecycle from development to production.
“Cloudera has a clear vision of what the future looks like for AI-driven companies that leverage investments in big data and put them to work in actual ML-based applications using Cloudera tools for data science and machine learning,” said Sivan Metzger, CEO of ParallelM. “We are excited to be part of this vision and to put our industry-leading MLOps platform, MCenter, to work for Cloudera’s customers.”