With Elasticsearch machine learning one can build regression and classification models for data analysis and inference. Accurate prediction models are often too complex to understand simply by looking at their definition. Using feature importance, introduced in Elastic Stack 7.6, we can now interpret and validate such models.
In part 1 we talked about the industrial applications and benefits that 5G and fast compute at the edge will bring to AI products. In this part we will go deeper into how you can benefit from this new opportunity.
In part 1 we talked about the industrial applications and benefits that 5G and fast compute at the edge will bring to AI products. In part 2 we went deeper into how you can benefit from this new opportunity. In this part we will focus on the key technical barriers that 5G and Edge compute remove for AI applications.
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.
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.
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.
Maybe you’re interested in finding out more about deep learning? Maybe your current ML analytics are running too slowly or crushing your CPU and RAM? Or perhaps your boss has told you that they need an AI-based app so they can show off to their boss (who will then brag about it to their boss)?
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 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.
Earlier this year, I predicted that Artificial intelligence (AI) would move from innovation labs to mainstream workflow projects this year. In IT organizations, I believe service management teams will lead this trend. Here’s why.
