The ITOM Podcast returns with yet another episode to help you eliminate all the remote work woes in your IT environment. In the last episode, we discussed in detail about the key areas enterprises need to monitor to ensure endpoint security, measures to adopt to ensure cybersecurity while rolling out BYOD policies, and the crucial factors that IT leaders need to know about in the post-pandemic era with respect to IT security.

As work from home has ballooned in 2020, virtual methods for communicating with colleagues have become more critical than ever. Same goes for all the useful productivity and collaboration tools at our disposal. The emerging downside is the difficulty of finding needed information among so many tools. Compounding the problem is the tendency for info to get siloed off by department.

We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.

In today's world, the need for organisations to have comprehensive cybersecurity solutions in place has never been greater. New vulnerabilities are exposed on a daily basis, and data breaches pose not only monetary but reputational risk.

Markley Group is a Boston-based data center leader that provides co-location and private cloud services through a three-million square foot data center portfolio. The company was founded in 1991, when there was no such thing as the cloud. Today, many of its customers are experimenting in public cloud but often have workload requirements which are better suited or more affordable running on private infrastructure, according to Patrick Houston, Director of Engineering at Markley Group.

These release notes describe new features, improvements and fixed issues in Pandora FMS NG 748. They also provide information about upgrades and describe some workarounds for known issues.

In this blog post series, I’ve explored the relationship between observability and a set of software delivery lifecycle practices that help organizations adopt DevOps practices and change their ways of working from being project centric to product-centric. I started with Site Reliability Engineering, then considered Value Stream Management (VSM) and finish with this post on Continuous Integration and Delivery (CI/CD). Defining Continuous Integration

dbaddon.mp4 - Google Drive Sign in Most modern web applications are heavily reliant on persisting data with relational databases, and so it’s no surprise that a large part of application performance monitoring relates to keeping an eye on database performance to ensure that our SQL queries are as efficient as possible. With this in mind, Scout features a Database Add-on module, and in this video, we are going to take a closer look at what it has to offer.

Keeping track of what’s going on in Kubernetes isn’t easy. It’s an environment where things move quickly, individual containers come and go, and a large number of independent processes involving separate users may all be happening at the same time. Container-based systems are by their nature optimized for rapid, efficient response to a heavy load of requests from multiple users in a highly abstracted environment and not for high-visibility, real-time monitoring.

If you’re here, it means that your application is a hit, coming through a long way of development and deployments. Your application is finally in a stage where you or your team need to set up more servers than you can handle manually, and you have to provision them fast. There’s also the need to make sure that all of them have the same configuration, packages, and versions in order for your application to have the same behavior in all of them.