Concept

Deployment Frequency

How often code is deployed to production

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Concept

Deployment Frequency

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Concept

Deployment Frequency

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How often code is deployed to production

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2 Episodes
1 Related Concepts
5 Frameworks
14 Practices

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Transcript Mentions

DORA 4, there are four key metrics. There's two speed metrics, deployment frequency and lead time. So code commit to code deploy. There's stability metrics, MTTR and change fail rate. If those are used to assess the speed of the pipeline and the general performance of the pipeline, that's great. If you're trying to use those to understand... Because implied in that is feedback loops, right, because you used to kind of get feedback from customers. But we can't just use that blindly now when we're using AI, as an example, because we have feedback loops much earlier and not even just at the local build and test phase. We have feedback loops throughout, and even sometimes in the middle of some of the pipeline, that we really want to leverage in ways that weren't as useful before. I won't say they weren't possible, but we just didn't really focus there.

Nicole ForsgrenSource summary

Source summary: Measures how often deployments occur.

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  • How to measure AI developer productivity in 2025 | Nicole Forsgren · discusses · Deployment Frequency
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · discusses · Deployment Frequency
  • Lead Time · relates to · Deployment Frequency
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · features · Nicole Forsgren
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · features · Nicole Forsgren
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · references · Good Strategy Bad Strategy
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · references · Accelerate
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · references · Accelerate
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · mentions framework · DORA
  • How to measure and improve developer productivity | Nicole Forsgren (Microsoft Research, GitHub, Google) · mentions framework · DORA
  • How to measure AI developer productivity in 2025 | Nicole Forsgren · mentions framework · SPACE

Source mentions

Podcast excerpts and source summaries from the explorer. Entries marked “Source summary” are summaries, not transcript quotations.

How to measure AI developer productivity in 2025 | Nicole Forsgren

Nicole Forsgren · 00:15:01

DORA 4, there are four key metrics. There's two speed metrics, deployment frequency and lead time. So code commit to code deploy. There's stability metrics, MTTR and change fail rate. If those are used to assess the speed of the pipeline and the general performance of the pipeline, that's great. If you're trying to use those to understand... Because implied in that is feedback loops, right, because you used to kind of get feedback from customers. But we can't just use that blindly now when we're using AI, as an example, because we have feedback loops much earlier and not even just at the local build and test phase. We have feedback loops throughout, and even sometimes in the middle of some of the pipeline, that we really want to leverage in ways that weren't as useful before. I won't say they weren't possible, but we just didn't really focus there.