Operations | Monitoring | ITSM | DevOps | Cloud

In a perfect world, understanding how to manage a network would be a breeze. On your first day of managing a network, you’d find tons of documentation on the IT infrastructure waiting for you. Login credentials would be securely recorded and ready for review. Sadly, we don’t live in a perfect world. That’s why managing a new network can be tough—especially if you’re joining a brand new IT team or taking on a new client and aren’t sure what’s been done before.

In 2018, Honeycomb co-founder & CTO Charity Majors wrote a blog post titled, “An Engineer’s Bill of Rights (and Responsibilities).” We’ve recently updated and reposted it.

Dataflow is a fully managed stream and batch processing service from Google Cloud that offers fast and simplified development for data-processing pipelines written using Apache Beam. Dataflow’s serverless approach removes the need to provision or manage the servers that run your applications, letting you focus on programming instead of managing server clusters. Dataflow also has a number of features that enable you to connect to different services.

What is AIOps? How does an AIOps platform help your observability practice? AIOps platforms analyze telemetry and events, and identify meaningful patterns that provide insights to support proactive responses. AIOps platforms have five characteristics:1 The above is Gartner’s definition and is part of the Gartner® “Market Guide for AIOps Platforms.” The Gartner definition is also aligned with our view.

If you asked your engineering team how well they can handle all of the security and observability data they’re managing, would you get a resounding “Yeah boss, we’re good to go!” in response? Possible, but unlikely. Chances are they feel like they’re stuck on a boat that’s taking on water, spending their day using tiny buckets to scoop some of it out, with no way to plug any of the leaks.

This article was written by Shane from Infosys. Infosys is a global IT Leader, headquartered in India, with over 200,000 employees and a focus on digital transformation, AI/ML, and Analytics. Our organization faces challenges when working with data to assist with proactive anomaly detection, triaging incidents to accommodate for data and volume growth, and maintaining high availability and SLA’s for a near 100% uptime.

This article was originally published in The New Stack and is reposted here with permission. You may be familiar with live examples of machine learning (ML) and deep learning (DL) technologies, like face recognition, optical character recognition OCR, the Python language translator, and natural language search (NLS). But now, DL and ML are working toward predicting things like the stock market, weather and credit fraud with astounding accuracy.