Operations | Monitoring | ITSM | DevOps | Cloud

Deploy ImageLabeller with Gitlab

Follow along with this step-by-step video series as Warren Marusiak, a senior technical evangelist, demonstrates pushing a code change to production using Bitbucket Pipelines CI/CD. To demonstrate how to develop, deploy, and manage applications using Jira Software and various connected tools, our team created ImageLabeller, a simple demo application built on AWS that uses machine learning to apply labels to images.

Practical Tips & Tricks for Speeding Up Your CI/CD Pipelines

When developing software and maintaining CI/CD and testing pipelines we are often compelled to increase our test coverage by adding more tests, and therefore improve our apps’ quality. After all, more automation equals better software, right? There’s a flipside to this equation however, and a point at which we start seeing diminishing returns from each test we add. Taken to extreme, these diminishing returns begin to actively harm our ability to deliver working software.

Dashboard Fridays: Sample Microsoft Teams Dashboard

Join SquaredUp's Adam Kinniburgh and Purdue University's Daniel Parrott as they showcase this sample Microsoft Teams dashboard used by Purdue to visualize key Microsoft Teams usage metrics for their online classrooms. Built using SquaredUp, this dashboard keeps track of the total number of Teams and which are empty, allowing them to pinpoint issues with the data load to create the teams or update issues. The dashboard also monitors for empty class sections to help identify issues with the class selection process.

Continuous Software Pipelines: Why Enterprises Are Going Cloud-Native 2021 Dev Week Cloud Keynote

Why are enterprise organizations making a move from on-premise solutions to completely cloud-native? What does that mean for improving, scaling, and securing their CI/CD pipelines? And what exactly is continuous packaging, anyway? Join Dan McKinney in this Dev Week Cloud session he answers all of these questions, helping attendees understand the true difference between cloud-hosted and cloud-native, how to get started with migrating to a cloud-native solution, and the true benefits of being entirely within the cloud.

Cloud-Native Pipelines: Secure Software Delivery, Made Simple Dev Week Cloud Workshop Session

Your entire tech stack is likely in the Cloud - so why aren’t your software packages? Whether you’re currently on-premise, have your own in-house solution or have a bit of a hybrid set up, join us in this session to explore why the future is cloud-native, what the benefits of this are over cloud-hosted, and how to easily set up a secure, cloud-native software pipeline in 60 seconds.

"Build It Yourself, They Said. It Will Be Worth It, They Said" Dev Week Enterprise Keynote Session

“We’ll build it ourselves!” We’ve all heard it, seen it, and likely been directly impacted by the decision to build a custom, in-house solution rather than use an existing one. Whether it’s a CI/CD tool, artifact management solution, or even the entire DevOps tech stack, it’s a common misconception that building it internally is easier, cheaper, and faster. When, in fact, the complete opposite is true!

Continuous Software Pipelines: Why Enterprises Are Going Cloud-Native Dev Week Enterprise Open Talk

Your entire tech stack is likely in the Cloud - so why aren’t your software packages? Whether you’re currently on-premise, have your own in-house solution or have a bit of a hybrid set up, join us in this session to explore:- Why enterprise organizations are making the move from on-premise solutions to completely Cloud-Native ones- What this means for improving, scaling, and securing their CI/CD pipelines- What the benefits of this are over cloud-hosted- How to easily set up a secure, cloud-native software pipeline in 60 seconds.

How to Develop and Deploy AI/ML Workloads at Scale - Prototype to Production in Days, not Months

Explore how organizations can develop and deploy machine learning (ML) workloads at scale on top of Kubernetes in NVIDIA DGX systems, while satisfying the organization’s security and compliance requirements, thus minimizing operational friction and meeting the needs of all the different teams involved in a successful ML effort.