Chasing a Hidden Gem: Graph Analytics with Splunk's Machine Learning Toolkit

Do you like gems? Perfectly cut diamonds? Crystal clear structures of superior beauty? You do? Then join me on a 10 minute read about a quest for hidden gems in your data: graphs! Be warned, it is going to be a mysterious journey into data philosophy. But you will be rewarded with artifacts that you can use to start your gemstone mining journey today.


How I Built a Machine Learning Pipeline on AWS for Under $7 a Day

Andreessen Horowitz recently published a blog about the Heavy Cloud Costs and Scaling Challenges of The New Business of AI, in which they describe how AI companies are facing cloud cost challenges, which are impacting their margins. As someone who used to manage a fully home-grown on-site distributed speech recognition platform for an industry leader, I know firsthand that ML can be expensive and challenging to maintain. However, it doesn’t have to be.


Why Every Web Developer Should Explore Machine Learning

If software's been eating the world for the past twenty years, it's safe to say machine learning has been eating it for the past five. But what exactly is machine learning? Why should a web developer care? This article by Julie Kent answers these questions. I don't have kids yet, but when I do, I want them to learn two things: Whether or not you believe that the singularity is near, there's no denying that the world runs on data.


Distributed model training using Dask and Scikit-learn

The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.


Contribute to Netdata's machine learning efforts!

Netdata contributors have greatly influenced the growth of our company and are essential to our success. The time and expertise that contributors volunteer are fundamental to our goal of helping you build extraordinary infrastructures. We highly value end-user feedback during product development, which is why we’re looking to involve you in progressing our machine learning (ML) efforts!


AI Meets Kubernetes: Install JupyterHub with Rancher

AI and Machine Learning are becoming critical differentiators in the technology landscape. By their nature, AI and ML are computation hungry workloads. They require best-in-class distributed computing environments to thrive. AI and ML present a perfect use case for Kubernetes, the distributed computing platform engineered at Google to run their massive workloads.


DevOps Makes Optimization Extremely Difficult for Humans - but a Breeze for AI

One costly DevOps myth is the idea that going fast means compromising on fundamentals: “Oh, my application is rebuilt ten times per day, so it doesn’t make sense to try and optimize it.” The reality is the exact opposite. If your apps are this dynamic, you actually need more optimization. You need it to be seamless, automated, and smart, with help from Artificial Intelligence and Machine Learning (AI & ML).