Operations | Monitoring | ITSM | DevOps | Cloud

Distributed Machine Learning With PySpark

Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.

Key Kubernetes Concepts

Cloud computing, containerization, and container orchestration are the most important trends in DevOps. Whether you’re a data scientist, software developer, or product manager, it’s good to know Docker and Kubernetes basics. Both technologies help you collaborate with others, deploy your projects, and increase your value to employers. In this article, we’ll cover essential Kubernetes concepts. There are a lot of Kubernetes terms, which can make it intimidating.

Kubernetes vs Docker: How to Choose

If you’re thinking about using containers to manage an application, there are a lot of options for technologies to use. It can be difficult to even know where to begin to make a decision. One common question is whether someone should use Docker vs Kubernetes for managing their application containers. This is a misleading question. In truth, Docker and Kubernetes aren’t competing technologies. There’s no need for them to face off.

Easy A/B Testing with PlanOut

So you want to A/B test your web app. The idea is simple, but the details can get messy, and you don’t want to re-invent the wheel. Services like Optimizely are pretty good, but they can be expensive and full of features you don’t need immediately. In this post, we’ll share how Sentry wrote an experimentation system with minimal work.