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Auvik Use Case: Gain Visibility Into the Internet of Things

There seems to be a smart version of everything these days. From coffee machines to aquarium thermometers, if you can think of a device that can benefit from an internet connection, it probably already exists. It’s not really a surprise. The IoT market is on the brink of explosion, as Intel projects 200 billion IoT devices will be added to our networks by 2020, up from 15 billion in 2015. And they’re not all for personal use.

A Cool Milestone for Monitoring as Code: Checkly Recognized a THIRD Time by Gartner!

Hello, Checkly community and Monitoring as Code (MaC) aficionados! We have some exhilarating news that we can't wait to share. Our mascot is sporting sunglasses today because Checkly has been named in Gartner®'s 2023 Cool Vendors in Monitoring and Observability: Where Awareness Meets Understanding report!

Deep Learning for Time Series Data (O'Reilly Artificial Intelligence Conference)

Arun Kejariwal and Ira Cohen, both thought leaders in the deep learning space, share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data. Present at the 2019 O'Reilly Artificial Intelligence Conference.

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.

Logz.io and Microsoft Azure: A Proud Partnership in Open Source

Today, I’m excited to announce a partnership between Logz.io and Microsoft Azure. With this partnership, Logz.io is now offering Azure customers a fully managed, scalable machine data analytics platform built on ELK and Grafana. What does that mean? Azure customers can now easily deploy, run, and scale ELK without the hassle and pain of maintaining and managing the stack themselves.