2020 may well go down as the year where what seems impossible today, did become possible tomorrow. It’s been a year filled with disruption and uncertainty. One day we were all going to the office, and the next we were working from home. Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things.
Michelin is a global organization well known for its high-performance tires, popular travel guides, and numerous innovations around materials, services, and connected solutions. Many consumers recognize the French brand by the famous “Michelin Man” mascot, whose body is composed of stacked white tires.
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 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 🙁
The recent global pandemic caused by the COVID-19 virus has threatened the sanctity of our humanity and the well-being of our societies at large. Similar to times of war, the pandemic has also given us the opportunity to appreciate the things we take for granted such as health workers, food suppliers, drivers, grocery store clerks and many others who are in the frontlines keeping us safe at this difficult time, Salute!
This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.
If you write software for a living, you probably have a bias toward coded tests and against all things codeless. Most software engineers who become test engineers trust themselves to write well-designed structured code. Some people see record-and-playback as cheating, demeaning, or otherwise indicative of poor workmanship. Yet, research shows that test code maintenance costs correlate directly to the number of lines of written test code.
