The usual method of delivering software into production consists of developers creating the artifact, then handing it off to a team of DevOps engineers who sweat bullets as they write scripts and carefully deploy the code into production. This method is time-consuming, error prone, and is no longer suitable for the accelerated pace of business. Engineering teams are under immense pressure to ship code faster, and can't afford a slow, painful process. How can this process be streamlined using machine learning by giving developers the power to deploy their own code directly into production?
In this presentation, DevOps Expert, Nick Durkin, discusses the need to combine the practice of continuous delivery with machine learning to give the power directly to the developer team--including actually how to do it. He will discuss:
- How to create a failure strategy around product deployments, giving the business confidence that "push button deployments" will not result in performance problems
- How both response times and custom events can be teased out of existing monitoring and logging tools using the correct algorithms
- How to create a dashboard that gives engineering leaders visibility into every deployment, including what happened
Is it truly possible to achieve Continuous Delivery Nirvana using Machine Learning--giving the power directly to the development team?
