mloptiflow 0.0.13

ML Lifecycle Management Framework

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 mloptiflow:

pip install mloptiflow

3. initialize a new project and choose a name and paradigm (currently supported paradigms are: tabular_regression, tabular_classification):

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

4. cd into your project directory and (if using poetry) update name field in pyproject.toml file:

cd <your-project-name>

[tool.poetry]
name = "<your-project-name>"

5. optionally, create a root package for the project and add __init__.py file:

mkdir <your-project-name>
touch <your-project-name>/__init__.py

6. install dependencies:

poetry install --no-root

or (if you created root package):

poetry install

or if using pip:

pip install -r requirements.txt

Usage

1. run the application:

streamlit run app.py

or:

poetry run streamlit run app.py

2. optionally, adjust Dockerfile to your needs if you want to run the inference application in a containerized environment:

# mainly the WORKDIR
WORKDIR /<your-project-name>

3. build the container image:

docker build -t <your-project-name> .

4. run the container image:

docker run -p 8501:8501 <your-project-name>

Support

• TBA

Roadmap

• TBA

Contributing

• TBA

License

MIT