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Debugging Machine Learning Models with Python

Debugging Machine Learning Models with Python

By : Ali Madani
4.9 (16)
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Debugging Machine Learning Models with Python

Debugging Machine Learning Models with Python

4.9 (16)
By: Ali Madani

Overview of this book

Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.
Table of Contents (26 chapters)
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1
Part 1:Debugging for Machine Learning Modeling
5
Part 2:Improving Machine Learning Models
10
Part 3:Low-Bug Machine Learning Development and Deployment
15
Part 4:Deep Learning Modeling
19
Part 5:Advanced Topics in Model Debugging

Data versioning

We have different stages in the machine learning life cycle, from data collection and selection to data wrangling and transformation, in which the data gets prepared step by step for model training and evaluation. Data versioning helps us maintain data integrity and reproducibility throughout these processes. Data versioning is the process of tracking and managing changes in datasets. It involves keeping a record of different versions or iterations of the data, allowing us to access and compare previous states or recover earlier versions when needed. We can reduce the risk of data loss or inconsistencies by ensuring that changes are properly documented and versioned.

There are data versioning tools that can help us in managing and tracking changes in the data we want to use for machine learning modeling or processes to assess the reliability and fairness of our models. Here are some popular data-versioning tools:

  • MLflow: We introduced MLflow for experiment...

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