Enterprise data cloud company Cloudera, 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.
Typically, we and the industry refers to MLOps as AIOps – we will be using the terms interchangeably in this post.
“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.”
“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,” said Doug Cutting, chief architect at Cloudera. “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.”
Industry Call for Standards
“Open source and open APIs have powered the growth of data science in business. But deploying and managing models in production is often difficult because of technology sprawl and siloing,” said Peter Wang, CEO of Anaconda. “Open standards for ML operations can reduce the clutter of proprietary technologies and give businesses the agility to focus on innovation. We are very pleased to see Cloudera lead the charge for this important next step.”
“As leaders in creating a machine learning oriented data strategy across our organization, we know what is required to address the challenges with deploying ML models into production at scale and building an ML-driven business,” said Daniel Stahl, SVP model platforms at Regions Financial Corporation. “A fundamental set of model design principles enables the repeatable, transparent, and governed approaches necessary for scaling model development and deployment. We join Cloudera in calling for open industry standards for machine learning operations.”
“At Santander, we focus on using machine learning to preemptively fight fraud and protect our customers,” said Luan Vasconcelos Corumba, data science leader for fraud prevention at Santander Bank. “Because there are many different types of fraud across many channels; scaling and maintaining this effort requires dynamic approaches to monitoring and governing models with sometimes hundreds of features to check on an ongoing weekly basis. We endorse these standards because establishing and implementing open universal standards for our production ML workflows can not only help us better protect our customers but will also enable our teams to drive adoption and deliver cost-effective, accurate predictions continuously.”
The company welcomes your outreach for more information.
Our take: We are a bit confused about the term usage of ML instead of AI as in AIOps – knowing ML is a subset of AI. In addition, there is a lot of talk about security in this news from Cloudera – we have become accustomed to seeing AI in cybersecurity via anomaly detection while AIOps is generally operational only, not focused on security.
We know there is potential overlap but vendors in anomaly detection and AIOps generally don’t overlap much.
MLOps deals specifically with ML, where AIOps include MLOps plus other AI analytic technologies (e.g. performance baselining, anomaly detection, automated root cause analysis, predictive insights).Update from Cloudera Dec 12, 2019
Either way – we welcome these discussions at the industry’s live event:
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