Overview | Running the Demo | Pipeline Output
This demo demonstrates how to build a full end-to-end automated-ML (AutoML) pipeline using scikit-learn and the UCI Iris data set.
The generated machine-learning (ML) pipeline, which also includes CI and data ingestion, consists of the following steps:
- Create an Iris data-set generator (ingestion) function.
- Ingest the Iris data set.
- Analyze the data-set features.
- Train and test the model using multiple algorithms (AutoML).
- Deploy the model as a real-time serverless function.
- Test the serverless function's REST API with a test data set.
See the sklearn-project.ipynb notebook for details.
To run the demo, download the sklearn-project.ipynb notebook into an empty directory and execute the code cells sequentially.
The output plots can be viewed as static HTML files in the plots directory.
