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Mark Zandi

Chief Economist · Moody's Analytics

280 activities
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Mark Beyer

Gartner

8 activities
Mark Austin

Mark Austin

VP Data Science · AT&T

7 activities
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Activities & events

Title & Speakers Event
Event O'Reilly Data Engineering Books 2022-06-25
Ten Things to Know About ModelOps 2022-06-25
Larry Derany – author , Thomas Hill – author , Mark Palmer – author

The past few years have seen significant developments in data science, AI, machine learning, and advanced analytics. But the wider adoption of these technologies has also brought greater cost, risk, regulation, and demands on organizational processes, tasks, and teams. This report explains how ModelOps can provide both technical and operational solutions to these problems. Thomas Hill, Mark Palmer, and Larry Derany summarize important considerations, caveats, choices, and best practices to help you be successful with operationalizing AI/ML and analytics in general. Whether your organization is already working with teams on AI and ML, or just getting started, this report presents ten important dimensions of analytic practice and ModelOps that are not widely discussed, or perhaps even known. In part, this report examines: Why ModelOps is the enterprise "operating system" for AI/ML algorithms How to build your organization's IP secret sauce through repeatable processing steps How to anticipate risks rather than react to damage done How ModelOps can help you deliver the many algorithms and model formats available How to plan for success and monitor for value, not just accuracy Why AI will be soon be regulated and how ModelOps helps ensure compliance

data data-engineering data-models AI/ML Analytics Data Science React
Getting DataOps Right 2019-07-25
Mark Marinelli – author , Michael Stonebraker – author , Nik Bates-Haus – author , Andy Palmer – author , Liam Cleary – author

Many large organizations have accumulated dozens of disconnected data sources to serve different lines of business over the years. These applications might be useful to one area of the enterprise, but they’re usually inaccessible to other data consumers in the organization. In this short report, five data industry thought leaders explore DataOps—the automated, process-oriented methodology for making clean, reliable data available to teams throughout your company. Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli from Tamr use real-world examples to explain how DataOps works. DataOps is as much about changing people’s relationship to data as it is about technology, infrastructure, and process. This report provides an organizational approach to implementing this discipline in your company—including various behavioral, process, and technology changes. Through individual essays, you’ll learn how to: Move toward scalable data unification (Michael Stonebraker) Understand DataOps as a discipline (Nik Bates-Haus) Explore the key principles of a DataOps ecosystem (Andy Palmer) Learn the key components of a DataOps ecosystem (Andy Palmer) Build a DataOps toolkit (Liam Cleary) Build a team and prepare for future trends (Mark Marinelli)

data data-engineering DataOps
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