Article Deep Learning Experimentation Machine Learning 
Cloudera
FEBRUARY 16, 2022
In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. In this article, we explore model governance, a function of ML Operations (MLOps). Machine Learning Model Lineage. Machine Learning Model Visibility . Machine Learning Model Explainability .
Smart Data Collective
DECEMBER 24, 2020
Machine learning is the driving force of AI. It allows humans to essentially teach software in a matter of weeks what a human would take decades to learn. AI and machine learning are changing the world we live in and altering the way we do things. Some grad students have already learned this the hard way.
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O'Reilly on Data
MARCH 19, 2020
We use it as a data source for our annual platform analysis , and we’re using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O’Reilly [1]. that support unsupervised learning. What’s driving this growth?
O'Reilly on Data
JULY 28, 2020
In this article, we turn our attention to the process itself: how do you bring a product to market? It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Without clarity in metrics, it’s impossible to do meaningful experimentation. Identifying the problem.
Rocket-Powered Data Science
JULY 6, 2021
1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles. 2) MLOps became the expected norm in machine learning and data science projects. 7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all.
Domino Data Lab
AUGUST 22, 2019
Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.
O'Reilly on Data
OCTOBER 19, 2021
Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract.
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