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    <title>Pipeline Conversations - Episodes Tagged with “Agents”</title>
    <link>https://podcast.zenml.io/tags/agents</link>
    <pubDate>Mon, 09 Dec 2024 08:00:00 +0100</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.
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    <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.
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  <title>LLM Agents in Production: Architectures, Challenges, and Best Practices</title>
  <link>https://podcast.zenml.io/llmops-db-agents</link>
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  <pubDate>Mon, 09 Dec 2024 08:00:00 +0100</pubDate>
  <author>ZenML GmbH</author>
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  <itunes:author>ZenML GmbH</itunes:author>
  <itunes:subtitle>An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices.</itunes:subtitle>
  <itunes:duration>32:37</itunes:duration>
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  <description>&lt;p&gt;An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.&lt;/p&gt;

&lt;p&gt;Please read the full blog post &lt;a href="https://www.zenml.io/blog/llm-agents-in-production-architectures-challenges-and-best-practices" target="_blank" rel="nofollow noopener"&gt;here&lt;/a&gt; and the associated LLMOps database entries &lt;a href="https://zenml.io/llmops-database" target="_blank" rel="nofollow noopener"&gt;here&lt;/a&gt;. &lt;/p&gt;
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    <![CDATA[<p>An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.</p>

<p>Please read the full blog post <a href="https://www.zenml.io/blog/llm-agents-in-production-architectures-challenges-and-best-practices" rel="nofollow">here</a> and the associated LLMOps database entries <a href="https://zenml.io/llmops-database" rel="nofollow">here</a>.</p>]]>
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    <![CDATA[<p>An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.</p>

<p>Please read the full blog post <a href="https://www.zenml.io/blog/llm-agents-in-production-architectures-challenges-and-best-practices" rel="nofollow">here</a> and the associated LLMOps database entries <a href="https://zenml.io/llmops-database" rel="nofollow">here</a>.</p>]]>
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