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İçerik Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
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An Introduction to the MLOps Tool Evaluation Rubric

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Manage episode 489496433 series 1264075
İçerik Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Organizations looking to build and adopt artificial intelligence (AI)–enabled systems face the challenge of identifying the right capabilities and tools to support Machine Learning Operations (MLOps) pipelines. Navigating the wide range of available tools can be especially difficult for organizations new to AI or those that have not yet deployed systems at scale. This webcast introduces the MLOps Tool Evaluation Rubric, designed to help acquisition teams pinpoint organizational priorities for MLOps tooling, customize rubrics to evaluate those key capabilities, and ultimately select tools that will effectively support ML developers and systems throughout the entire lifecycle, from exploratory data analysis to model deployment and monitoring. This webcast will walk viewers through the rubric's design and content, share lessons learned from applying the rubric in practice, and conclude with a brief demo.

What Attendees Will Learn:

• How to identify and prioritize key capabilities for MLOps tooling within their organizations

• How to customize and apply the MLOps Tool Evaluation Rubric to evaluate potential tools effectively

• Best practices and lessons learned from real-world use of the rubric in AI projects

  continue reading

174 bölüm

Artwork
iconPaylaş
 
Manage episode 489496433 series 1264075
İçerik Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Organizations looking to build and adopt artificial intelligence (AI)–enabled systems face the challenge of identifying the right capabilities and tools to support Machine Learning Operations (MLOps) pipelines. Navigating the wide range of available tools can be especially difficult for organizations new to AI or those that have not yet deployed systems at scale. This webcast introduces the MLOps Tool Evaluation Rubric, designed to help acquisition teams pinpoint organizational priorities for MLOps tooling, customize rubrics to evaluate those key capabilities, and ultimately select tools that will effectively support ML developers and systems throughout the entire lifecycle, from exploratory data analysis to model deployment and monitoring. This webcast will walk viewers through the rubric's design and content, share lessons learned from applying the rubric in practice, and conclude with a brief demo.

What Attendees Will Learn:

• How to identify and prioritize key capabilities for MLOps tooling within their organizations

• How to customize and apply the MLOps Tool Evaluation Rubric to evaluate potential tools effectively

• Best practices and lessons learned from real-world use of the rubric in AI projects

  continue reading

174 bölüm

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