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How to Operationalize Your Data Science with Model Ops

TIBCO - Connected Intelligence

Just as you wouldn’t train athletes and not have them compete, the same can be said about data science & machine learning (ML). You wouldn’t spend all this time and money on creating ML models without putting them into production, would you? Reading Time: 3 minutes. Deploy/Integrate.

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How MLOps Enables Machine Learning Production at Scale

DataRobot

This is why businesses are increasingly investing in machine learning operations (MLOps): IDC predicts by 2024, 60% of enterprises will have operationalized their ML workflows by using MLOps. How would it cope with scale and managing additional models? 2 What Is MLOps and How Does It Help?

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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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Technology Trends for 2023

O'Reilly Media - Ideas

Methodology This report is based on our internal “units viewed” metric, which is a single metric across all the media types included in our platform: ebooks, of course, but also videos and live training courses. Data engineering was the dominant topic by far, growing 35% year over year.

Trends 134