A recent press release states, “Cloudera, the enterprise data cloud company, today asks for industry participation in defining universal open standards for machine learning operations (MLOps) and machine learning model governance. By contributing to these standards, the community can help companies make the most of their machine learning platforms and pave the way for the future of MLOps. Join the conversation by contacting mlops-dev@cloudera.com. ‘Machine learning models are already part of almost every aspect of our lives from automating internal processes to optimizing the design, creation, and marketing behind virtually every product consumed,’ said Nick Patience, founder and research vice president, software at 451 Research. ‘As ML proliferates, the management of those models becomes challenging, as they have to deal with issues such as model drift and repeatability that affect productivity, security and governance. The solution is to create a set of universal, open standards so that machine learning metadata definitions, monitoring, and operations become normalized, the way metadata and data governance are standardized for data pipelines’.”

Doug Cutting, chief architect at Cloudera, commented, “At Cloudera, we don’t want to solve the challenge of deploying and governing machine learning models at scale only for our customers, we agree it needs to be addressed at the industry level. Apache Atlas is the best positioned framework to integrate data management and explainable, interoperable, and reproducible MLOps workflows… The Apache Atlas (Project) fits all the needs for defining ML metadata objects and governance standards. It is open-source, extensible, and has pre-built governance features.”

Read more at cloudera.com.

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