Harness Invests in Automating Continuous Delivery

Fresh off raising an additional $60 million in funding, Harness plans to make more investments in machine learning algorithms to advance continuous delivery through automation.

Harness CEO Jyoti Bansal said the startup, which he co-founded with Rishi Singh, a former DevOps platform architect at Apple, is now in a position to invest more aggressively in additional engineering and sales resources needed to expand adoption of the cloud-based service, which provides a level of abstraction through which much of the continuous delivery process can be automated using drag-and-drop graphical tools. Bansal and Singh co-founded Harness after AppDynamics, provider of an application performance management (APM) platform Bansal also co-founded and served as chairman of the board, was sold to Cisco Systems.

Bansal said Harness takes advantage of YAML files and machine leaning algorithms to present DevOps teams a set of visual tools accessed via a cloud service that enables them to build sophisticated pipelines as quickly as 10 minutes. That approach promises to significantly advance DevOps adoption by making it easier for IT operations teams to set up those pipelines as part of continuous delivery process without needing deep programming expertise.

While there’s been a lot of progress made in terms of driving adoption of best continuous integration practices among developers, the continuous delivery side of the DevOps equation has proved much more difficult to achieve. Most IT organizations rely on multiple platforms that have their own unique attributes. As such, IT operations teams typically have created their own scripts or rolled out automation frameworks such as Ansible to allow them to automate some aspects of the application deployment process.

However, Bansal noted, custom scripts typically don’t scale very well and usually have not been well-documented. What’s more, frameworks such as Ansible are still too low-level to enable DevOps teams to build pipelines in minutes. The approach Harness has designed enables DevOps teams to describe their intent using a set of visual tools, which then employs machine learning algorithms to automate the deployment process regardless of the underlying platform, he said.

That approach is further augmented by a set of rules that can be added to make sure security and compliance requirements are met, he noted, adding Harness also provides views into how processes are being automated to provide DevOps teams with the confidence required to trust a highly automated platform.

Bansal said the goal is to not just make it simpler to build and deploy pipelines, but also maintain them in what have become highly dynamic IT environments where there is simply too much data that needs to be tracked for human administrators to effective manage relying on low-level tools and frameworks. Rather than having to build and deploy the framework needed to achieve that goal, he said, Harness is making it possible to automate the continuous delivery process using a cloud service on demand.

It may take a while for IT organizations get comfortable with the level of automation being enabled by machine learning algorithms. But like it or not, it’s already becoming apparent the ability to dynamically manage IT at scale is clearly on the cusp of making another significant leap forward.

— Mike Vizard