Model-Packaging

Model packaging refers to the process of organizing and preparing machine learning models for deployment and use in real-world applications. This involves creating a structured format that includes not only the model itself but also essential metadata, such as versioning, dependencies, and performance metrics. Effective model packaging enhances reproducibility, facilitates collaboration among data scientists, and ensures that models can be easily integrated into various systems. By standardizing how models are packaged, organizations can streamline their workflows, improve model management, and promote transparency in AI systems, ultimately leading to more reliable and efficient applications.

Box Model

 Codecademy

The Box Model is a CSS layout mechanism that the web browser uses to render content organized by box-shaped elements. Each element is made of four specific areas: width and height : The width and heig...

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Model Cards

 Kaggle Learn Courses

Introduction A **model card** is a short document that provides key information about a machine learning model. Model cards increase transparency by communicating information about trained models to ...

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Model Cards

 Kaggle Learn Courses

Introduction A **model card** is a short document that provides key information about a machine learning model. Model cards increase transparency by communicating information about trained models to ...

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Model Cards

 Kaggle Learn Courses

Introduction A **model card** is a short document that provides key information about a machine learning model. Model cards increase transparency by communicating information about trained models to ...

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Building and Sharing a Model Card for Reproducibility

 Python in Plain English

Model Card In machine learning, a model card is like a box of Lego pieces and instructions. It’s a document that explains how a machine-learning model was built, trained, and tested. This way, someone...

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Is My Model Really Better?

 Towards Data Science

Why ML models that look good on paper are not guaranteed to work well in production Continue reading on Towards Data Science

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Model Distillation

 Towards AI

Making AI Models Leaner and Meaner: A go-to approach for small and medium businesses | Practical guide to shrinking AI Models without losing their Intelligence Image Source: Author 1\. Introduction A...

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Foundation Models

 Codecademy

Foundation models power AI systems that are trained on text, images, code, and other formats of data. These models pre-train on large amounts of unlabeled data from the internet. They can then be fine...

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Deploying Your Models (Cheap and Dirty Way) Using Binder

 Towards AI

So you’ve built your model, and now you want to deploy it for testing or to show it to someone like your mom or grandpa, who may not be comfortable using a collaborative notebook? No worries at all; y...

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Packages

 Codecademy

In Python, packages are a way to organize related modules into a directory hierarchy. Packages allow for modular code organization, making projects more readable, reusable, and maintainable. A package...

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Keeping your production model fresh

 Towards Data Science

A great model needs love and attention if it is stay as useful as it was on day one for its whole life. Any statistical or machine learning model will experience a loss of performance over time as…

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Bagging on Low Variance Models

 Towards Data Science

Bagging (also known as bootstrap aggregation) is a technique in which we take multiple samples repeatedly with replacement according to uniform probability distribution and fit a model on it. It…

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