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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Versioning and storing your model

As we have been working through this book, there has been one glaring issue that you might have noticed – when you closed your integration development environment, terminal, or Jupyter notebook, your model and data were gone. We won't go into the more involved topics of working and saving information on databases or other persistence layers, but there are some quite simple things you can do to create save points along the way.

Understanding the value of versioning your model

As you've worked through everything from data engineering to building models in this book, you have realized that there are a lot of iterations that happen. It's called data science, but there is also an art to guessing a path and trying to know where to go next. You've tried to make educated guesses with hyperparameters and model families, and kept the original dataset open to come back to as needed. This was all needed in case you were wrong....

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