Testing ML Pipeline Best Practices to Scale with LakshmiThejaswi Narasannagari
TestGuild Automation Podcast
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Mastering the Machine Learning Pipeline
This chapter explores the critical components of the machine learning pipeline, detailing the feature, training, and inference layers essential for model development and deployment. It emphasizes the importance of testing and evaluation strategies to ensure data quality, prevent overfitting, and maintain model performance post-deployment. Additionally, the discussion highlights the role of guardrails in ensuring model integrity and the need for continuous monitoring to address biases and model drift.
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