mloptiflow 0.0.22

Dynamic MLOps Framework with Integrated CLI for Automated ML Project Inception, Kafka-Driven Real-Time Model Monitoring, and Adaptive Canary Deployment Architectures

MLOPTIFLOW

Dynamic MLOps Framework with Integrated CLI for Automated ML Project Inception, Kafka-Driven Real-Time Model Monitoring, and Adaptive Canary Deployment Architectures

Installation

1. create a new virtual environment with python ^3.11 and activate it

2. install poetry:

pip install poetry

3. install mloptiflow:

pip install mloptiflow

4. initialize a new project and choose a name and paradigm (currently supported paradigms are: tabular_regression, tabular_classification, demo_tabular_classification)[demo ones are just a minimalistic examples of the paradigm]:

mloptiflow init <your-project-name> --paradigm=<paradigm-name>

5. cd into your project directory:

cd <your-project-name>

6. install dependencies:

poetry install

or if using pip:

pip install -r requirements.txt

DEMO Test

1. create a new virtual environment with python ^3.11 and activate it

2. install poetry:

pip install poetry

3. install mloptiflow:

pip install mloptiflow

4. initialize a new project with the name demo-project and paradigm demo_tabular_classification:

mloptiflow init demo-project --paradigm=demo_tabular_classification

5. cd into your project directory:

cd demo-project

6. install dependencies:

poetry install

7. run the training pipeline:

mloptiflow train start

8. run and test the inference API:

mloptiflow deploy start --with-api-test

Usage

• TBA

Support

• TBA

Roadmap

• TBA

Contributing

• TBA

License

MIT