Data Engineering eBook Organization 
Cloudera
SEPTEMBER 17, 2020
For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. CDP data lifecycle integration and SDX security and governance.
Cloudera
NOVEMBER 19, 2021
Organizations need to usher their ML models out of the lab (i.e., Even though organizations know that deployment is where the business value happens, model deployment is one of the first pitfalls for many organizations. Organizations must think about an ML model in terms of its entire life cycle.
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Cloudera
APRIL 13, 2021
Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. 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.
Cloudera
JUNE 2, 2021
Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable.
TIBCO - Connected Intelligence
AUGUST 10, 2020
In today’s data-driven world, the winners will be the organizations that successfully gain a competitive advantage from their data, and the losers will fall behind. . Taking action to leverage your data is a multi-step journey, outlined below: First, you have to recognize that sticking to the status quo is not an option.
TIBCO - Connected Intelligence
MAY 20, 2020
Model Ops (aka ML Ops) ensures that models continue to deliver value to the organization. Organizations need to realize the value of data science and machine learning models holistically, rather than as simply a process of developing models. Realize the value of data science through Model Ops.
Cloudera
NOVEMBER 16, 2022
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.” . Or play defense — manage risk for the organization? Are you playing offense or defense?
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