This blog post will provide guidance to administrators currently using or interested in using Kafka nodes to maintain cluster changes as they scale up or down to balance performance and cloud costs in production deployments. Kafka brokers contained within host groups enable the administrators to more easily add and remove nodes. This creates flexibility to handle real-time data feed volumes as they fluctuate.
Kong Gateway provides dynamic plugin ordering allowing administrators to control plugin execution order. Dynamic plugin ordering was added in Kong Enterprise 3.0 and the full technical reference is available in the official documentation. (Check out the overview of what’s new in Kong Gateway 3.0 announced at Kong Summit 2022.)
Principal Component Analysis (PCA) is one of the most commonly used unsupervised machine learning algorithms across a variety of applications: exploratory data analysis, dimensionality reduction, information compression, data de-noising, and plenty more. In this blog, we will go step-by-step and cover: Before we delve into its inner workings, let’s first get a better understanding of PCA. Imagine we have a 2-dimensional dataset.
Accepting user input is critical to modern Rails applications, but without validations, it can cause problems. In this article, learn how to use `ActiveModel` validations to ensure the data you process is safe.
We’ve been working on many new features and improvements for Loadero, both large and small. And now we are excited to share the updates and some information about even bigger updates coming soon. Here is what changed in Loadero recently.
Achieving optimal software reliability and quality management processes sit at the core of a memorable digital experience. Quality management in software can be summarized in two points: Stakeholders and management always want their digital products to successfully launch. Software testing is normally seen as rejecting builds and stretching out the delivery date. Why is that?
It has been months ago when Toloka and ClearML met together to create this joint project. Our goal was to showcase to other ML practitioners how to first gather data and then version and manage data before it is fed to an ML model. We believe that following those best practices will help others build better and more robust AI solutions. If you are curious, have a look at the project we have created together.
Tell us if this sounds familiar. You’ve found an awesome data set that you think will allow you to train a machine learning (ML) model that will accomplish the project goals; the only problem is the data is too big to fit in the compute environment that you’re using. In the day and age of “big data,” most might think this issue is trivial, but like anything in the world of data science things are hardly ever as straightforward as they seem.
Databases are important for every organization. Learn more about what databases are and the different types available.
