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Intro
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Agenda
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Context
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Problem Statement
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Typical Problem
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Data Set
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Moral Challenges
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AI ML
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Metric choice
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Prediction
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False positives
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Fscore
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Baseline
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Iterating
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Issues
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Theories
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Qualification challenges
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Scalability
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Questions
Description:
Explore a DevConf.CZ 2023 conference talk that delves into the challenges and pivotal decisions in Machine Learning projects. Learn from Juan Díaz and Katya Gordeeva's experiences in extracting value from OpenShift cluster data, including their project on predicting upgrade issues. Gain insights on maintaining focus on business goals, defining success metrics early, and choosing appropriate technologies. Discover the importance of stakeholder communication, addressing moral challenges, and overcoming qualification and scalability issues in ML implementations. Understand why 85% of ML projects fail and how to avoid common pitfalls through practical lessons learned in a real-world scenario.

How Implementing Machine Learning Ended Up with an If-Else - Lessons from OpenShift Cluster Prediction

DevConf
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