Kedro Product Manager

Senior Director of Product Management, Astronomer · 1 appearance on the record.

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operatorengineerexecutive@yetudada ↗LinkedIn ↗astronomer.io ↗

At QuantumBlack, she managed and open-sourced Kedro, a Python framework for data science and machine learning pipelines. At Astronomer, she leads the build experience and developer tooling for Apache Airflow, including agentic AI tooling.

5statements → 1claims → 0claims resolved → 3.8/5average certainty → 2.2/5average debate potential →

1 not checkable as stated how the 1 claim stands · each chip opens the sources

1 assertion · 3 insights · 1 disclosure · every statement was checked. The predictions and assertion are the 1 claim: statements the public record can support or contradict. 0 are resolved, and 1 names no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Kedro argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: speaking style how? →

243 words/min while actually speaking · 42 um and uh per 1k words

No argument clarity score for Kedro Product Manager: only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 4,028 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

What they said5 Episodes1

Everything Kedro Product Manager said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight

Dada argues building data pipelines is harder than machine learning itself

“And this is where we believe that machine learning is not really the hard part, but building and maintaining the data pipeline is”

Kedro Product Manager Feb 17, 2021 ▶ 3:18 Introducing Kedro

Disclosure

Kedro leaves pipeline scheduling and failure monitoring to Airflow and Dagster

“How, what time will this pipeline run? How will I know if it failed? We'll leave those tools to Dagster, Airflow, Prefect, Luigi, and many others to actually handle for you, because they do that really well.”

Kedro Product Manager Feb 17, 2021 ▶ 6:25 Introducing Kedro

Insight

Dada asserts notebooks are challenging for reproducible, production-ready data pipelines

“In terms of actually building your full pipeline and notebook that's where things get a little bit complicated because notebooks are a little bit challenging for reproducible and what we call production-ready code.”

Kedro Product Manager Feb 17, 2021 ▶ 10:27 Introducing Kedro

Insight

Dada says data science experiment parameters must be separated from codebases

“So your experiment parameters for data science workflow should also be outside of your code base because we really believe it helps you make code that is generalizable and reusable by removing things that loading and load the code for loading and saving your d…”

Kedro Product Manager Feb 17, 2021 ▶ 12:25 Introducing Kedro

Assertion Not checkable as stated

Dada argues AWS SageMaker is a deployment target, not a code-structuring tool

“We use, we think of Kedra or SageMaker as a deployment target for Kedra. So still that whole process of like creating like, well document, well structured, Modular data science code, but still having the freedom to deploy it on SageMaker is how we see those ro…”

Kedro Product Manager Feb 17, 2021 ▶ 16:27 Introducing Kedro

Appearances (1)

Episode	Date	Speaking time
Introducing Kedro	Feb 17, 2021	19m

Kedro Product Manager

The record, in short

How they sound: speaking style how? →

Appearances (1)

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