The network is the foundation for all applications. With the increase in distributed applications and their hybrid nature, the network has become even more important. Delegating more and more tickets to AI will not only help reduce pressure on support team resources, but also fundamentally shift operations from being focused on reactive troubleshooting to proactive remediation.
The shine on AI and ML projects has never been brighter. The C-suite is excited about the prospects of these projects and is throwing money at them. Why then, with all this enthusiasm, can’t AI/ML projects gain more traction across the business? Why are so many of them failing? The short answer: your business executives are not getting timely insights or sometimes even the right insights.
Modern complex systems are easy to develop and deploy but extremely difficult to observe. Their IT Ops data gets very messy. If you have ever worked with modern Ops teams, you will know this. There are multiple issues with data, from collection to processing to storage to getting proper insights at the right time.
I was having a conversation with a CxO level customer as part of an AIOps/Observability workshop, and from what I could tell, most are confused about how to properly operationalize cloud-native production environments – especially the monitoring/observability portion. Here is how the conversation went.
Every business now depends on IT. Efficient IT Operations is mandatory for all businesses, especially those operating in a hybrid mode – a mix of existing data centers and multi-cloud locations. As with any business process, IT operations can be augmented with machine learning-based solutions. IT is particularly fertile ground for AI as it is mostly digital, has seemingly endless processes requiring automation and there are gigantic amounts of data to process.
Observability is an emerging set of practices, platforms, and tools that goes beyond monitoring to provide insight into the internal state of systems by analyzing external outputs. Monitoring has been a core function of IT for decades, but old approaches have become inadequate for a variety of reasons—cloud deployments, agile development methodology, continuous deployments, and new DevOps practices among them.
AIOps vs Observability vs Monitoring – What Is The Difference? Are You Using The Right One For Your Enterprise?
This article was originally published in Forbes on Feb 2, 2021 In the last few months, I have been analyzing and writing a research report for GigaOm in this space, which is due to be released soon. I looked at about 30+ vendors in this space as part of that […]
Originally published in APMDigest.com on Sep 30, 2020 Most business executives are worried about the competition taking them down. What they don’t realize is, their own IT can do an equal amount of damage. Imagine this: if your rideshare app is constantly down, would you rather wait for it to […]
As enterprises are starting to engage machine learning models and embed them into heavy-duty production systems, they face a lot of hurdles. Especially, MLOps lacks enterprise-grade feature stores to stores, search, replace, and collaborate on ML models. This article will explore that problem in detail, and explore one such solution […]
The global pandemic has radically changed the way enterprise IT services are produced, consumed, and managed. It also has exposed a glaring difference between the “the haves and have-nots” of the software development and operations teams. Engineering teams riding on CI/CD and DevOps waves are starting to see the full potential […]