The Cloudera Support Organization has always strived to not only provide solutions to our customers but to also deliver helpful knowledge. One of the primary sources of that knowledge comes from our Knowledge Articles. This content is created and curated by our knowledgeable Support Staff based on real-world experience coming from support cases. These Knowledge Articles have proven to be invaluable to our Support Staff over the years.
Linear regression, alongside logistic regression, is one of the most widely used machine learning algorithms in real production settings. Here, we present a comprehensive analysis of linear regression, which can be used as a guide for both beginners and advanced data scientists alike.
We recently Googled the manufacturing use case “predictive maintenance” and was astonished by the results there were 82 MILLION results returned. Next, we Googled “process optimization” and it yielded even more results – 302 MILLION. Clearly, these use cases are top of mind in today’s manufacturing landscape, considering digital transformation will deliver $11 Trillion USD in economic value by 2025.
In support of our partnership with AWS and the AWS DevOps Competency program, we’re re-examining how some of our major customers are using Kong with AWS. To this end, let’s look at our popular case study with TUNE.
The Cloudera Data Warehouse (CDW) service is a managed data warehouse that runs Cloudera’s powerful engines on a containerized architecture. It is part of the new Cloudera Data Platform, or CDP, which went live on Microsoft Azure earlier this year. The CDW service lets you meet SLAs, onboard new use cases with zero friction, and minimize cost. Today, we are pleased to announce the general availability of CDW on Microsoft Azure.
We are all familiar with this scenario, you work on your training code, fix “all” of the bugs (the ones you know about), wait for a few iterations, see that batch size wasn’t wrong and nothing blows up, and then you happily go home. However, when you come back into the office the next day look at your loss and test accuracy you’re horrified to find that the experiment crashed on the first test cycle because you pointed your test set in the wrong folder 🙁