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Herding Code 235: Matthew Renze on Data Science for Software Developers
Episode 235
Download / Listen: Herding Code 235: Matthew Renze on Data Science for Software Developers
At DevSum Stockholm, Jon talks to Matthew Renze (@matthewrenze ) about data science practices to improve both the products they are creating and their software development practices.
Topics:
- (00:20) Matthew explains how he's been speaking to software developers about applying data science practices to improve both the products they are creating and their software development practices.
- (00:40) Data science can add intelligence to applications, machine learning to automate decision-making processes, and deep learning to modify the user interface using anticipatory design.
- (03:57) The other side to this is using data science to help build software. The DevOps pipeline provides a lot of objective measures to help improve our software development processes and practices.
- (05:51) Software telemetry data can help us prioritize the time we spend on features towards those that are actively used.
- (07:12) Jon asks which terms he really needs to understand as a developer. Matthew defines data science, machine learning, deep learning, and reinforcement learning. They discuss how text suggestions and language understanding have progressed, and where generated text can and can't help.
- (13:55) Machine learning can be used for good and for evil - for instance, it's now possible to forge video in a way that's really tough to detect. What do we do now? Matthew talks about what we can do as developers to educate those around us and apply ethics to the software we contribute to.
- (19:50) How do we handle things like legal liability for machines that are making decisions, like self-driving cars? Matthew puts it in historical context and talks about how we'll need to adapt our society to accommodate.
- (24:12) Jon asks where to get started applying data science today. Matthew gives some pointers on where to get started learning, and how to start with some quick wins like A/B testing and objective software quality metrics.
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By Herding Code
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3131 ratings
Episode 235
Download / Listen: Herding Code 235: Matthew Renze on Data Science for Software Developers
At DevSum Stockholm, Jon talks to Matthew Renze (@matthewrenze ) about data science practices to improve both the products they are creating and their software development practices.
Topics:
- (00:20) Matthew explains how he's been speaking to software developers about applying data science practices to improve both the products they are creating and their software development practices.
- (00:40) Data science can add intelligence to applications, machine learning to automate decision-making processes, and deep learning to modify the user interface using anticipatory design.
- (03:57) The other side to this is using data science to help build software. The DevOps pipeline provides a lot of objective measures to help improve our software development processes and practices.
- (05:51) Software telemetry data can help us prioritize the time we spend on features towards those that are actively used.
- (07:12) Jon asks which terms he really needs to understand as a developer. Matthew defines data science, machine learning, deep learning, and reinforcement learning. They discuss how text suggestions and language understanding have progressed, and where generated text can and can't help.
- (13:55) Machine learning can be used for good and for evil - for instance, it's now possible to forge video in a way that's really tough to detect. What do we do now? Matthew talks about what we can do as developers to educate those around us and apply ethics to the software we contribute to.
- (19:50) How do we handle things like legal liability for machines that are making decisions, like self-driving cars? Matthew puts it in historical context and talks about how we'll need to adapt our society to accommodate.
- (24:12) Jon asks where to get started applying data science today. Matthew gives some pointers on where to get started learning, and how to start with some quick wins like A/B testing and objective software quality metrics.