Introduction(00:00:00 - 00:03:15) - Validating Machine Learning Model and Avoiding Common Challenges

Introduction(00:00:00 - 00:03:15)
Validating Machine Learning Model and Avoiding Common Challenges

Learn how to validate your machine learning model and how to avoid common challenges.

Building a good and stable machine learning model is hard. The wide variety of challenges in the process includes biases when building or splitting the datasets, data leakages, data quality, and integrity issue...
Learn how to validate your machine learning model and how to avoid common challenges.

Building a good and stable machine learning model is hard. The wide variety of challenges in the process includes biases when building or splitting the datasets, data leakages, data quality, and integrity issues, drifts, model performance stability, and many more.

In this session we’ll explore these types of challenges, give real-life examples of such faults, and suggest a structure for building tests for these types of issues, to enable validating them efficiently. We’ll include a hands-on demonstration of running validation tests during the ML research phase (which you can follow along by running it locally). By the end of this session, you’ll have the knowledge about which issues to look out for to avoid critical problems, along with the tools for how to do so efficiently.

Table of Contents:
00:00 Introduction
03:15 ML Failures and Motivation
18:10 ML Validation/Testing
24:35 Deepcheck Packages
29:15 Live Code Example
46:32 QnA

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Download Live Code: https://bit.ly/3Of8am0
Deepchecks Github: https://github.com/deepchecks/deepchecks
Deepchecks Docs: https://docs.deepchecks.com/
Deepchecks Checks Demo: https://checks-demo.deepchecks.com/
Deepcheck Slack Community: https://www.deepchecks.com/slack
Deepchecks Website: https://www.deepchecks.com/
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#machinelearningmodel #machinelearning #llm #modelvalidation

#machine learning model #ML model #model validation #machine learning pitfalls #Deepcheck Packages #machine learning testing #data leakages #data quality #data integrity #model performance #ml #machine learning tutorial

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