https://github.com/schmidtbri/ml-base

Base classes and utilities that are useful for deploying ML models.
https://github.com/schmidtbri/ml-base

ml-deployment mlops

Last synced: about 2 months ago
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Base classes and utilities that are useful for deploying ML models.

• Host: GitHub
• URL: https://github.com/schmidtbri/ml-base
• Owner: schmidtbri
• License: bsd-3-clause
• Created: 2020-05-24T14:05:11.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2023-08-11T20:28:29.000Z (over 1 year ago)
• Last Synced: 2024-08-04T03:12:07.804Z (5 months ago)
• Topics: ml-deployment, mlops
• Language: Python
• Homepage: https://schmidtbri.github.io/ml-base/
• Size: 1.32 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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# ml-base

**ml-base** is a package that provides base classes and utilities that are useful for deploying machine learning models.

## Installation 

The easiest way to install ml-base is using pip

```bash

pip install ml-base

```

## Usage

There are several examples of how to use the ml-base framework in the 

[documentation](https://schmidtbri.github.io/ml-base/).

## Development

First, download the source code with this command:

```bash

git clone https://github.com/schmidtbri/ml-base

```

Then create a virtual environment and activate it:

```bash

# go into the project directory

cd ml-base

make venv

source venv/bin/activate

```

Install the dependencies:

```bash

make dependencies

```

## Testing 

To run the unit test suite execute these commands:

```bash

# first install the test dependencies

make test-dependencies

# run the test suite

make test

# clean up the unit tests

make clean-test

```