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Getting started

AWS MLOps package is implemented to help you like a framework for deploying what it is necessary to manage your models.

The goal is to implement this package to keep your focus on your prep and test code using the AWS SageMaker services, AWS Step Functions and AWS Lambda.

It is part of the educational repositories to learn how to write stardard code and common uses of the TDD and CI / CD.

Prerequisites

You can use Serverless framework for deploying the AWS services: if you want to use the guide below, you have to install npm (Node Package Manager) before.

If you want to use another AWS tool, you can see the repository aws-tool-comparison before to implement your version.

Installation

The package is not self-consistent. So you have to download the package by github and to install the requirements before to deploy the example on AWS:

git clone https://github.com/bilardi/aws-mlops
cd aws-mlops/
npm install
export AWS_PROFILE=your-account
export STAGE=studio
bash example/deploy.sh

Or if you want to use this package into your code, you can install by python3-pip:

pip3 install aws_mlops
python3
>>> import aws_mlops
>>> help(aws_mlops)

Read the documentation on readthedocs for

  • Usage

  • Development

Change Log

See CHANGELOG.md for details.

License

This package is released under the MIT license. See LICENSE for details.

Metadata

Release files for aws-mlops 1.2.2

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Source distribution for aws-mlops 1.2.2
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aws_mlops-1.2.2-py3-none-any.whl Python 3 none any Details for aws_mlops-1.2.2-py3-none-any.whl

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Release files / aws-mlops-1.2.2.tar.gz

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Release files / aws_mlops-1.2.2-py3-none-any.whl

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Size 22.0 kB
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