O'Reilly Media - Data
JUNE 5, 2018
Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.
Altexsoft
JULY 23, 2020
When speaking of machine learning, we typically discuss data preparation or model building. The fusion of terms “machine learning” and “operations”, MLOps is a set of methods to automate the lifecycle of machine learning algorithms in production — from initial model training to deployment to retraining against new data.
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Confluent
FEBRUARY 6, 2019
Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.
Cloudera
NOVEMBER 19, 2021
We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Continuous Operations for Production Machine Learning (COPML) helps companies think about the entire life cycle of an ML model.
The Crazy Programmer
NOVEMBER 6, 2021
hence, if you want to interpret and analyze big data using a fundamental understanding of machine learning and data structure. You are also under TensorFlow and other technologies for machine learning. However, you need to learn about continuous integrations, logging, collaboration, and more to start with it.
DevOps.com
OCTOBER 22, 2020
Harness, at its {Unscripted} 2020 conference today, announced its plans in the fourth quarter to make available as a beta a module that leverages machine learning algorithms to optimize build and test cycles on the Harness Continuous Integration (CI) Enterprise platform.
TechCrunch
JUNE 10, 2022
While terms like machine learning are not new, specific solutions areas like “decision intelligence” don’t fall within a clear category. Companies in even newer categories can map to terms like continuous integration or container management. Salesforce created the category they dominated.
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