Software Engineering Daily

Continuous delivery is a model for deploying small, frequent changes to an application. In a continuous delivery workflow, code changes that are pushed to a repository set off a build process that spins up a new version of the application. Testing is performed against that new build before advancing it to production, merging it with the existing codebase.

Many continuous delivery products are getting built today because it is a wide open space–much like cloud providers or monitoring tools. There are subjective product and engineering decisions to be made depending on the audience for the product.

Heroku Flow is a continuous delivery platform built on top of Heroku, a platform as a service. Andy Appleton is an engineer at Heroku and he joins the show to describe how Heroku Flow was built. Two years of work went into the project from initial conception to launch.

Full disclosure: Heroku is a sponsor of Software Engineering Daily.

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Reinforcement learning is a type of machine learning where a program learns how to take actions in an environment based on how that program has been rewarded for actions it took in the past. When program takes an action, and it receives a reward for that action, it is likely to take that action again in the future because it was positively reinforced.

Michal Kempka is a computer scientist work works on VizDoom, an AI research platform for reinforcement learning, with co-creators Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski. VizDoom is based on the first-person dungeon game Doom. In VizDoom, an autonomous agent navigates through a maze avoiding enemies.

Reinforcement learning is a widely used tool for machine learning, and we will be doing more shows in the future that explain how it works in further detail.

Cornell University Library: VizDoom

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In its most basic definition, machine learning is a tool that makes takes a data set, finds a correlation in that data set, and uses that correlation to improve a system. Any complex system with well-defined behavior and clean data can be improved with machine learning.

Several precipitating forces have caused machine learning to become widely used: more data, cheaper storage, and better tooling. Two pieces of tooling that have been open sourced from Google help tremendously: Kubernetes and TensorFlow.

Kubernetes is not a tool for machine learning, but it simplifies distributed systems operations, unlocking more time for engineers to focus on things that are not as commodifiable–like tweaking machine learning parameters. TensorFlow is a framework for setting up machine learning systems.

Machine learning should affect every aspect of our lives–including tuxedo fitting. Generation Tux is a company that allows customers to rent apparel that historically has required in-person fitting. Using machine learning, they have developed a system that allows customers to get fit for an outfit without entering a brick-and-mortar store.

In this episode, Colan Connon and Thomas Bell from Generation Tux join to explain how Generation Tux adopted Kubernetes and TensorFlow, and how the company’s infrastructure and machine learning pipeline work.

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Every software company backs up critical data sources. Backing up databases is a common procedure, whether a company is in the cloud or on-prem. Backing up virtual machine instances is less common.

Rubrik is a company that is known for building backup infrastructure for enterprises. Their main product is an appliance that sits on prem at an enterprise and stores snapshots of virtual machines running within the enterprise. If a virtual machine dies, Rubrik can quickly restore the VM snapshot. The appliance also backs up to the cloud.

Kenny To is a founding engineer at Rubrik, and he joins the show to discuss backups and how Rubrik is engineered. Enterprises that start backing up to the cloud through Rubrik start a path towards potentially more cloud services. For enterprises that have not been able to move to the cloud yet, this can be an appealing opportunity.

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