Enterprise cloud data firm Cloudera has issued a call for industry participation to help define universal open standards for machine learning operations (MLOps) and machine learning model governance. MLOps revolves around implementing machine-learning in production, notably around the infrastructure and tooling needed to deploy machine-learning algorithms and data pipelines reliably and at scale. By leveraging the community, Cloudera hopes to help companies make the most of their machine learning platforms and pave the way forward for the future of MLOps. The challenge of deploying and governing machine learning models at scale needs to be addressed at the industry level, says Doug Cutting, the chief architect at Cloudera. He pointed to Apache Atlas as being the best-positioned framework to integrate data management for explainable, interoperable and reproducible MLOps workflows.

Machine learning (ML) has become one of the most critical capabilities for modern businesses to grow and stay competitive today. From automating internal processes to optimizing the design, creation and marketing processes behind virtually every product consumed, ML models have permeated almost every aspect of our work and personal lives -- and for businesses, the stakes have never been higher. Failing to adopt ML as a core competency will result in major competitive disadvantages that will define the next market leaders. Because of this, business and technology leaders need to implement ML models across their entire organization, spanning a large spectrum of use cases. However, this sense of urgency, combined with growing regulatory scrutiny, creates new and unique governance challenges that are currently difficult to manage.