This project demonstrates how to build an ML application and use MLOps to operationalize it.
The project contains four notebooks, in the following order:
- Exploratory Data Analysis
- Data preparation, training and evaluating a model
- Application Serving Pipeline
- Pipeline Automation and Model Monitoring
You can find the python source code under /src
This project can run in different development environments:
- Local computer (using PyCharm, VSCode, Jupyter, etc.)
- Inside GitHub Codespaces
- Sagemaker studio and Studio Labs (free edition) or other managed Jupyter environments
To get started, fork this repo into your GitHub account and clone it into your development environment.
To install the package dependencies (not required in GitHub codespaces) use:
make install-requirements
If you prefer to use Conda or work in Sagemaker use this instead (to create and configure a conda env):
make conda-env
Make sure you open the notebooks and select the
mlrunconda environment
The MLRun service and computation can run locally (minimal setup) or over a remote Kubernetes environment.
If your development environment support docker and have enough CPU resources run:
make mlrun-docker
MLRun UI can be viewed in: http://localhost:8060
If your environment is minimal or you are in Sagemaker run mlrun as a process (no UI):
[conda activate mlrun &&] make mlrun-api
For MLRun to run properly you should set your client environment, this is not required when using codespaces, the mlrun conda environment, or iguazio managed notebooks.
Your environment should include MLRUN_ENV_FILE=<absolute path to the ./mlrun.env file> (point to the mlrun .env file in this repo), see mlrun client setup instructions for details.
Note: You can also use a remote MLRun service (over Kubernetes), instead of starting a local mlrun, edit the mlrun.env and specify its address and credentials