article thumbnail

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.

article thumbnail

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.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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.

article thumbnail

Modernizing Data Pipelines using Cloudera Data Platform – Part 1

Cloudera

To keep up, data pipelines are being vigorously reshaped with modern tools and techniques. At Cloudera, we recently introduced several cutting-edge innovations in our Cloudera Data Engineering experience (CDE) as part of our Enterprise Data Cloud product — Cloudera Data Platform (CDP) — to serve the growing demands.

Data 92
article thumbnail

How to Sell the Business on Data Virtualization

TIBCO - Connected Intelligence

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. Your data demands, like your data itself, are outpacing your data engineering methods and teams. Data virtualization presents a compelling financial case.

article thumbnail

How to Operationalize Your Data Science with Model Ops

TIBCO - Connected Intelligence

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. In fact, many organizations have a dedicated Model Ops Engineer to facilitate this process.

Data 72
article thumbnail

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.” . Cloudera has written an ebook together with Corinium on the success factors for CDAOs.

Data 81