Companies invest in anomaly detection in order to proactively identify risks, such as revenue loss, customer churn and operational performance issues. Anomaly detection essentially enhances traditional BI and visualization tools, venturing beyond a summary view of your data. It constantly scans every metric, at a granular level, to find abnormalities. But in order for this technology to have an impact, you must be able to trust it.

Machine Learning (ML) algorithms are designed to automatically build mathematical models using sample data to make decisions. Rather than use specific instructions, they rely on patterns and inference instead. And the business applications abound. In recent years, companies such Google and Facebook have found ways to use ML to utilize the massive amounts of data they have for more profit.

We’re pleased to come home from DattoCon19 with memorable experiences. It was a well-curated conference, striking a fine balance between emerging MSP trends, informative sessions, networking and of course, entertainment. Here are my top takeaways from the event.

Enterprise IT teams are dealing with the daily challenges of alert floods, point tool sprawl, and overwhelming hybrid complexity. The recent OpsRamp State of AIOps report indicated that IT professionals are using AIOps tools for productivity gains from intelligent alerting (69%), faster root cause analysis (61%), and better infrastructure performance through anomaly detection (55%).

The introduction of Google Maps in 2005 changed the way we think about the internet. It’s hard to remember now, but there was a time where the internet was disconnected from the physical world. You might find a business’s website, and if you were lucky, they’d have an address included. A national chain of restaurants or grocery stores probably wouldn’t be able to tell you their nearest location to your home. All of that has changed, today.

he Graylog community is what makes the product so exciting. It is awesome to see our community members take the time to help everyone over on our community forums, twitter, reddit or on their own private channels. I wanted to take some time to highlight a blog post by Community member BlueTeamNinja (aka Big Abe) who, after tackling a Graylog deployment shared lessons learned from a non-Linux/non-Elk person.

Logging your Kubernetes clusters to LogDNA is already a breeze, and now the LogDNA Kubernetes agent Helm chart makes it even easier. Helm is the official package manager for Kubernetes. With Helm, deploying and managing Kubernetes applications is as simple as typing a single command. This makes deploying the LogDNA agent across your cluster absolutely effortless.

Yesterday our founder and CEO Nate Taggart announced his decision to step down from his post. While his presence will be missed by all of us, he made this choice in order for Stackery to bring in a new CEO with the experience and pedigree to steer the business to the next level.

Content delivery networks are distributed servers that help organizations improve access to various types of website, application and platform content. Small and medium-size companies sometimes assume that a single CDN is all that they need to support their demand, but a multi-CDN is not reserved for large enterprises alone. Several industries that particularly benefit from multi-CDNs are gaming and streaming.

Cloud is driving the way modern software is being built and deployed. At the forefront of this revolution is AWS, holding a whopping 33% of the cloud services market in Q1 2019. Considering AWS had a seven-year head start before its main competitors, Microsoft and Google, this dominance is not surprising. AWS offers, by far, the widest array of fully evolved cloud services, helping engineers to develop, deploy and run applications at cloud scale.