Automating vulnerable dependency upgrade with the CI/CD pipelines
The best DevOps models combine the strengths of IT teams and development teams to deliver great customer value.
A challenge most teams face is when the DevOps pipeline gets in the way of completing a deployment on time or not knowing the value DevOps automation brings to the board. DevOps must be scalable, innovative and champion a culture of positive change within an enterprise. If you achieve these conditions, DevOps can redefine the entire business model.
Kodez comes with several years of DevOps experience. Our consultants can equip your team with the independence to manage their DevOps pipelines/agents and enable faster/frequent production deployments. Our experts drive DevOps transformations across both major cloud platforms using tools such as Azure DevOps and BuildKite.
We helped FitnessPassport to establish their DevOps strategy with the Azure tool stack. It was a deployment from the ground up, which was an exciting challenge. Our consultants conducted a complete analysis of the existing infrastructure to create a DevOps strategy that met their needs. As a result, FitnessPassport is currently deploying multiple times a day to production.
Completely hands-off deployment was a primary goal for Seek, and we helped them do it! SeekBusiness did not have an automated build and deployment setup. This meant that the production deployment was slow and prone to error. We helped Seek to automate their SeekBusiness application and infrastructure deployment with AWS using BuildKite and a few other accessory AWS services. Now they can deploy to production during regular business hours with zero down-time. It is a significant step forward from their fortnightly manual deployment pattern.
Like FitnessPassport and SeekBusiness, you can be our next DevOps success story. It is time you get agility, speed and reliability across all deployments!
Treating your infrastructure as code is becoming more and more necessary these days. Writing these instructions becoming challenging too. In Azure we use ARM templates to define the resources and associate them with a deployment pipeline. But ARM templates are quite complicated and they are not everybody’s cup of tea.
Azure MachineLearning Service provides four main compute options each with a specific purpose attached to it. In this post we will go through each of those and see where we can occupy them in your ML experiments.
In the era of Industry 4.0 where data and predictive analytics players the major role, developing machine learning pipelines have become an essential in intelligent application development.
