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Make Your Models Matter: What It Takes to Maximize Business Value from Your Machine Learning Initiatives

Cloudera

As we explain in our eBook , COPML is a comprehensive approach to ML model development and operation that takes a structured approach to the “ML wrangling” problems many enterprises face. An enterprise machine learning workflow from data engineers to business users. COPML: The Glue That Holds It All Together.

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Once Upon a Time in the Land of Data

Cloudera

There is a clear consensus that data teams should express their goals and results in business value terms and not in technical, tactical descriptions, such as “improving data engineering” and “better master data management.” . In another firm, the head of the data office also owns the digital channels.

Data 77
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How to Operationalize Your Data Science with Model Ops

TIBCO - Connected Intelligence

Organizations need to realize the value of data science and machine learning models holistically, rather than as simply a process of developing models. While data science and ML processes are focused on building models, Model Ops focuses on operationalizing the entire data science pipeline within a business system.

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Apiumhub among top IT industry leaders in Code Europe event

Apiumhub

An International speaker, books & video author, and writer for Java Magazine, IBM Developer, Oracle, and InfoQ. His main work is software development consulting, which combines actually writing code with advising clients on how to do that better. Michal Lukaszewski – AI Manager @Intel Software developer, mentor, manager.

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Cloudera DataFlow’s key milestones and wins in 2020

Cloudera

Click here for a quick overview video or download our eBook to get more details. So, in order to share our thought leadership in this space, we decided to sponsor Flink Forward, an event focused on the Flink developer community. Our keynote , delivered by our VP of Engineering, Joe Witt, and Sr.

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Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines

Cloudera

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

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10 Steps to Achieve Enterprise Machine Learning Success

Cloudera

We recently published a Cloudera Special Edition of Production Machine Learning For Dummies eBook. Chapter six of the eBook focuses on the 10 steps for making ML operational. Your data scientists will want a platform and tools that give them practical access to data, compute resources, and libraries. Optimize later.