<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" encoding="UTF-8" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/" xmlns:atom="http://www.w3.org/2005/Atom/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:googleplay="http://www.google.com/schemas/play-podcasts/1.0" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:fireside="http://fireside.fm/modules/rss/fireside">
  <channel>
    <fireside:hostname>web01.fireside.fm</fireside:hostname>
    <fireside:genDate>Sat, 04 Apr 2026 04:04:26 -0500</fireside:genDate>
    <generator>Fireside (https://fireside.fm)</generator>
    <title>Pipeline Conversations - Episodes Tagged with “Zenml”</title>
    <link>https://podcast.zenml.io/tags/zenml</link>
    <pubDate>Mon, 26 Sep 2022 14:00:00 +0200</pubDate>
    <description>Pipeline Conversations brings you interviews with platform engineers, ML practitioners, and technical leaders building production AI systems. We dig into the real challenges of MLOps and LLMOps: orchestrating complex workflows on Kubernetes, fine-tuning and evaluating models at scale, and shipping AI that actually works. From ZenML.
</description>
    <language>en-us</language>
    <itunes:type>episodic</itunes:type>
    <itunes:subtitle>MLOps and LLMOps, from the trenches</itunes:subtitle>
    <itunes:author>ZenML GmbH</itunes:author>
    <itunes:summary>Pipeline Conversations brings you interviews with platform engineers, ML practitioners, and technical leaders building production AI systems. We dig into the real challenges of MLOps and LLMOps: orchestrating complex workflows on Kubernetes, fine-tuning and evaluating models at scale, and shipping AI that actually works. From ZenML.
</itunes:summary>
    <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/4/4d525632-f8ef-47c1-9321-20f5c498b1ac/cover.jpg?v=3"/>
    <itunes:explicit>no</itunes:explicit>
    <itunes:keywords>machine-learning, machinelearning, mlops, deeplearning, ai, artificialintelligence, artificial-intelligence, technology, tech, mlops, llmops</itunes:keywords>
    <itunes:owner>
      <itunes:name>ZenML GmbH</itunes:name>
      <itunes:email>podcast@zenml.io</itunes:email>
    </itunes:owner>
<itunes:category text="Technology"/>
<item>
  <title>ZenML MLOps Competition</title>
  <link>https://podcast.zenml.io/mlops-competition</link>
  <guid isPermaLink="false">20b2e352-4565-487d-ad6e-e0f865c75da5</guid>
  <pubDate>Mon, 26 Sep 2022 14:00:00 +0200</pubDate>
  <author>ZenML GmbH</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/20b2e352-4565-487d-ad6e-e0f865c75da5.mp3" length="6679573" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:season>2</itunes:season>
  <itunes:author>ZenML GmbH</itunes:author>
  <itunes:subtitle>So excited to be able to announce our :fire: AMAZING :fire: external judges for the ZenML Month of MLOps competition! We have a stellar panel of :sparkles: ML and MLOps heroes :sparkles: to help select the best pipelines from all of your submissions! </itunes:subtitle>
  <itunes:duration>8:13</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/4/4d525632-f8ef-47c1-9321-20f5c498b1ac/episodes/2/20b2e352-4565-487d-ad6e-e0f865c75da5/cover.jpg?v=1"/>
  <description>So excited to be able to announce our 🔥 AMAZING 🔥 external judges for the ZenML Month of MLOps competition! We have a stellar panel of ✨ ML and MLOps heroes ✨ to help select the best pipelines from all of your submissions! 
💥 Charles Frye, core instructor at the amazing Full Stack Deep Learning course
💥 Anthony Goldbloom, co-founder and former CEO of Kaggle
💥 Chip Huyen, author of 'Designing Machine Learning Systems' and co-founder of Claypot AI
💥 Goku Mohandas, founder of MadeWithML, another essential course in production ML
We're honoured to have them on board for the ride, and we can't wait to see all the amazing ML use cases and problems our competitors solve along the way!
To learn more about the competition and to sign up, visit https://zenml.io/competition 
</description>
  <itunes:keywords>zenml, mlops, open-source, competition</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>So excited to be able to announce our 🔥 AMAZING 🔥 external judges for the ZenML Month of MLOps competition! We have a stellar panel of ✨ ML and MLOps heroes ✨ to help select the best pipelines from all of your submissions! </p>

<p>💥 Charles Frye, core instructor at the amazing Full Stack Deep Learning course<br>
💥 Anthony Goldbloom, co-founder and former CEO of Kaggle<br>
💥 Chip Huyen, author of &#39;Designing Machine Learning Systems&#39; and co-founder of Claypot AI<br>
💥 Goku Mohandas, founder of MadeWithML, another essential course in production ML</p>

<p>We&#39;re honoured to have them on board for the ride, and we can&#39;t wait to see all the amazing ML use cases and problems our competitors solve along the way!</p>

<p>To learn more about the competition and to sign up, visit <a href="https://zenml.io/competition" rel="nofollow">https://zenml.io/competition</a></p><p>Links:</p><ul><li><a title="Sign Up for the Competition" rel="nofollow" href="https://zenml.io/competition">Sign Up for the Competition</a></li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>So excited to be able to announce our 🔥 AMAZING 🔥 external judges for the ZenML Month of MLOps competition! We have a stellar panel of ✨ ML and MLOps heroes ✨ to help select the best pipelines from all of your submissions! </p>

<p>💥 Charles Frye, core instructor at the amazing Full Stack Deep Learning course<br>
💥 Anthony Goldbloom, co-founder and former CEO of Kaggle<br>
💥 Chip Huyen, author of &#39;Designing Machine Learning Systems&#39; and co-founder of Claypot AI<br>
💥 Goku Mohandas, founder of MadeWithML, another essential course in production ML</p>

<p>We&#39;re honoured to have them on board for the ride, and we can&#39;t wait to see all the amazing ML use cases and problems our competitors solve along the way!</p>

<p>To learn more about the competition and to sign up, visit <a href="https://zenml.io/competition" rel="nofollow">https://zenml.io/competition</a></p><p>Links:</p><ul><li><a title="Sign Up for the Competition" rel="nofollow" href="https://zenml.io/competition">Sign Up for the Competition</a></li></ul>]]>
  </itunes:summary>
</item>
<item>
  <title>ZenML Recap with Adam and Hamza</title>
  <link>https://podcast.zenml.io/zenml-recap-adam-hamza</link>
  <guid isPermaLink="false">8ce789d5-23c4-4251-933d-c4797ea40684</guid>
  <pubDate>Thu, 28 Apr 2022 12:00:00 +0200</pubDate>
  <author>ZenML GmbH</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/8ce789d5-23c4-4251-933d-c4797ea40684.mp3" length="19127732" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>ZenML GmbH</itunes:author>
  <itunes:subtitle>Adam and Hamza return for a short discussion of what we've been busy working on during the previous few months, where we're going with ZenML and why it's so amazing to be building an open-source tool.</itunes:subtitle>
  <itunes:duration>25:31</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/4/4d525632-f8ef-47c1-9321-20f5c498b1ac/episodes/8/8ce789d5-23c4-4251-933d-c4797ea40684/cover.jpg?v=1"/>
  <description>Adam and Hamza return for a short discussion of what we've been busy working on during the previous few months, where we're going with ZenML and why it's so amazing to be building an open-source tool. 
</description>
  <itunes:keywords>zenml, mlops, open-source</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>Adam and Hamza return for a short discussion of what we&#39;ve been busy working on during the previous few months, where we&#39;re going with ZenML and why it&#39;s so amazing to be building an open-source tool.</p>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Adam and Hamza return for a short discussion of what we&#39;ve been busy working on during the previous few months, where we&#39;re going with ZenML and why it&#39;s so amazing to be building an open-source tool.</p>]]>
  </itunes:summary>
</item>
<item>
  <title>Monitoring Your Way to ML Production Nirvana with Danny Leybzon</title>
  <link>https://podcast.zenml.io/ml-monitoring-danny-leybzon</link>
  <guid isPermaLink="false">d5f677ea-92d0-421a-85e2-918f549ec265</guid>
  <pubDate>Thu, 16 Dec 2021 16:30:00 +0100</pubDate>
  <author>ZenML GmbH</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/d5f677ea-92d0-421a-85e2-918f549ec265.mp3" length="39705712" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>ZenML GmbH</itunes:author>
  <itunes:subtitle>This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.</itunes:subtitle>
  <itunes:duration>40:34</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/4/4d525632-f8ef-47c1-9321-20f5c498b1ac/episodes/d/d5f677ea-92d0-421a-85e2-918f549ec265/cover.jpg?v=1"/>
  <description>This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.
As such, we had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down, 
And of course we also discussed why monitoring and logging is actually a kind of baseline practice that should be part of any and every data scientist's toolkit. Luckily for us, Danny added in a bunch of examples from his wide experience doing all this in the real world. Special Guest: Danny Leybzon.
</description>
  <itunes:keywords>mlops, aws, machine-learning, zenml, industry</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.</p>

<p>As such, we had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down, <br>
And of course we also discussed why monitoring and logging is actually a kind of baseline practice that should be part of any and every data scientist&#39;s toolkit. Luckily for us, Danny added in a bunch of examples from his wide experience doing all this in the real world.</p><p>Special Guest: Danny Leybzon.</p><p>Links:</p><ul><li><a title="Danny D. Leybzon" rel="nofollow" href="http://web.dleybz.co/">Danny D. Leybzon</a></li><li><a title="whylogs · PyPI" rel="nofollow" href="https://pypi.org/project/whylogs/">whylogs · PyPI</a></li><li><a title="Data and AI Observability Platform - enabling MLOps | WhyLabs" rel="nofollow" href="https://whylabs.ai/">Data and AI Observability Platform - enabling MLOps | WhyLabs</a></li><li><a title="SLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=kTJASJbhpGs">SLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube</a></li><li><a title="Monitoring ML Models - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=Jn-RNwrP5O0">Monitoring ML Models - YouTube</a></li><li><a title="Monitoring ML Models in Production - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=oUcuilWWX78">Monitoring ML Models in Production - YouTube</a></li><li><a title="Machine Learning Models in Production - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=3lSVZi2Dcjg">Machine Learning Models in Production - YouTube</a></li><li><a title="Danny on LinkedIn" rel="nofollow" href="https://www.linkedin.com/in/dleybz/">Danny on LinkedIn</a></li><li><a title="Women&#39;s Clothes | Men&#39;s Clothes | Kid&#39;s Clothing Boxes | Stitch Fix" rel="nofollow" href="https://www.stitchfix.com/">Women's Clothes | Men's Clothes | Kid's Clothing Boxes | Stitch Fix</a></li><li><a title="zenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning." rel="nofollow" href="https://github.com/zenml-io/zenml">zenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.</a></li><li><a title="Terraform by HashiCorp" rel="nofollow" href="https://www.terraform.io/">Terraform by HashiCorp</a></li><li><a title="Zillow — A Cautionary Tale of Machine Learning - causaLens" rel="nofollow" href="https://www.causalens.com/blog/zillow-a-cautionary-tale-of-machine-learning/">Zillow — A Cautionary Tale of Machine Learning - causaLens</a></li><li><a title="Cloud Monitoring as a Service | Datadog" rel="nofollow" href="https://www.datadoghq.com/">Cloud Monitoring as a Service | Datadog</a></li><li><a title="Prometheus - Monitoring system &amp; time series database" rel="nofollow" href="https://prometheus.io/">Prometheus - Monitoring system &amp; time series database</a></li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.</p>

<p>As such, we had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down, <br>
And of course we also discussed why monitoring and logging is actually a kind of baseline practice that should be part of any and every data scientist&#39;s toolkit. Luckily for us, Danny added in a bunch of examples from his wide experience doing all this in the real world.</p><p>Special Guest: Danny Leybzon.</p><p>Links:</p><ul><li><a title="Danny D. Leybzon" rel="nofollow" href="http://web.dleybz.co/">Danny D. Leybzon</a></li><li><a title="whylogs · PyPI" rel="nofollow" href="https://pypi.org/project/whylogs/">whylogs · PyPI</a></li><li><a title="Data and AI Observability Platform - enabling MLOps | WhyLabs" rel="nofollow" href="https://whylabs.ai/">Data and AI Observability Platform - enabling MLOps | WhyLabs</a></li><li><a title="SLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=kTJASJbhpGs">SLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube</a></li><li><a title="Monitoring ML Models - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=Jn-RNwrP5O0">Monitoring ML Models - YouTube</a></li><li><a title="Monitoring ML Models in Production - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=oUcuilWWX78">Monitoring ML Models in Production - YouTube</a></li><li><a title="Machine Learning Models in Production - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=3lSVZi2Dcjg">Machine Learning Models in Production - YouTube</a></li><li><a title="Danny on LinkedIn" rel="nofollow" href="https://www.linkedin.com/in/dleybz/">Danny on LinkedIn</a></li><li><a title="Women&#39;s Clothes | Men&#39;s Clothes | Kid&#39;s Clothing Boxes | Stitch Fix" rel="nofollow" href="https://www.stitchfix.com/">Women's Clothes | Men's Clothes | Kid's Clothing Boxes | Stitch Fix</a></li><li><a title="zenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning." rel="nofollow" href="https://github.com/zenml-io/zenml">zenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.</a></li><li><a title="Terraform by HashiCorp" rel="nofollow" href="https://www.terraform.io/">Terraform by HashiCorp</a></li><li><a title="Zillow — A Cautionary Tale of Machine Learning - causaLens" rel="nofollow" href="https://www.causalens.com/blog/zillow-a-cautionary-tale-of-machine-learning/">Zillow — A Cautionary Tale of Machine Learning - causaLens</a></li><li><a title="Cloud Monitoring as a Service | Datadog" rel="nofollow" href="https://www.datadoghq.com/">Cloud Monitoring as a Service | Datadog</a></li><li><a title="Prometheus - Monitoring system &amp; time series database" rel="nofollow" href="https://prometheus.io/">Prometheus - Monitoring system &amp; time series database</a></li></ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Introducing ZenML</title>
  <link>https://podcast.zenml.io/introducing-zenml</link>
  <guid isPermaLink="false">094212f9-84ef-4cb3-8c2a-87230f3cef0a</guid>
  <pubDate>Fri, 19 Nov 2021 12:00:00 +0100</pubDate>
  <author>ZenML GmbH</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/094212f9-84ef-4cb3-8c2a-87230f3cef0a.mp3" length="22165206" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>ZenML GmbH</itunes:author>
  <itunes:subtitle>Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it's such a complicated problem to solve.</itunes:subtitle>
  <itunes:duration>22:18</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/4/4d525632-f8ef-47c1-9321-20f5c498b1ac/episodes/0/094212f9-84ef-4cb3-8c2a-87230f3cef0a/cover.jpg?v=1"/>
  <description>Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it's such a complicated problem to solve. 
</description>
  <itunes:keywords>zenml, mlops, machine-learning</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it&#39;s such a complicated problem to solve.</p>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it&#39;s such a complicated problem to solve.</p>]]>
  </itunes:summary>
</item>
  </channel>
</rss>
