Streamlined switching between local and cloud development stages within a unified project filesystem.
This Machine Learning project is meant to showcase a streamlined transition between different development stages. It features an intuitive mechanism for toggling between environments, ensuring an optimised workflow for both a local environment, as well as a cloud-based environment on Google Cloud Platform.
- Easy Environment Switching: Toggle
ENV_MODE
in the.env
file to seamlessly switch between debug, dev, staging, and prod environments. - centralized configuration:
.env
centralizes critical variables for automated resource naming, model configuration and versioning, and differentiated infrastructure specifications. - simplified command structure: execute build, run, and test commands with ease across training and inference stages.
-
clone the repository
-
configure the
.env
file according to your environment needs -
use the following commands to manage your project:
training:
make training-build
make training-run
make training-test
inference:
make inference-build
make inference-run
make inference-test
- preprocessors versioning
- training evaluation for staging and production stages using Vertex AI evaluation job (programatic implementation currently not supported)
- improved logs for debugging stage
- support for diferentiated training dataset for production stage