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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

By : Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
4.9 (11)
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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

4.9 (11)
By: Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

Overview of this book

AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks. Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.
Table of Contents (25 chapters)
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3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

Converting the model to ONNX

So far, you’ve seen how we can use Pickle to save a model and then load it in Python. However, Pickle has drawbacks, including being Python-specific. ONNX, on the other hand, is a format that is not Python-specific and can be used in other languages. Let’s see how we can use the ONNX format to work with our model.

We need to solve our task in two steps:

  • Convert the model to ONNX format.
  • Load the model and create a REST API in JavaScript.

Creating a model in ONNX format

Because we’re using a notebook, we need to install the dependencies, import the libraries we need, and then convert the model to ONNX format.

[Prompt]

What dependencies do I need for ONNX? Show me the Python code for installing those dependencies.

[End of prompt]

[Prompt response]

! pip install skl2onnx

[End of response]

[Prompt]

Show me how to import the libraries I need for ONNX and show me how to...

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