Since 45 minutes is nowhere near enough to compare the two tools (lesson re-learned the hard way while designing Service versus Studio), I decided to only compare their deployment capabilities, given an already trained model.
The resources used during the talk are available on GitHub.
- BmG.ipynb - the Jupyter notebook used to train and serialize the model
- iris.data.csv - the Iris data set, downloaded from here
- The Azure ML Studio experiment used to load the pickled model is available on the Azure AI Gallery
- conda_dependencies.yml - the Conda configuration file needed to create the Docker image in ML Service
- score.py - the interface for our model running on the Docker image
- input.json - input sample using the standard structure for Azure ML Studio; can also be used to invoke the web service deployed using Azure ML Service (this is why the code in score.py looks the way it does 🤓)
- During the talk I’ve demo-ed the code using Visual Studio Code, with the Azure Machine Learning and REST Client extensions
- I’m also linking to two tutorials, one for Machine Learning Studio and the other for Machine Learning Service, in case you want to learn more.