During this session Vlad will compare the two main options of doing machine learning in the Azure cloud - Azure Machine Learning Service and Azure Machine Learning Studio.
He will evaluate them from multiple viewpoints, such as ease of use, level of knowledge needed for getting started, the options available for ingesting, analysing, and preparing data, the differences in training and evaluating predictive models, and the particularities of exposing the models as web services, and allowing them to be consumed securely from external applications.
At the end of this talk, you will be able to make an informed decision on which of the two options best suits your use case.
These are the resources used for my Service versus Studio talk.
- First things first, I used the training dataset from Kaggle's Petfinder competition, available here, you will need this in order to be able to run the code.
- A sample configuration file is available in aml_config, all you need to do is fill in your own subscription/workspace details here.
- Code for Round 1 - Look and Feel is available here, incuding the training script and the Jupyter notebook used for integrating with Machine Learning Service
- Code for Round 2 - Analysing and Preparing Data is here, just a simple notebook with some very light data analysis
- Code for Round 3 - Training and Evaluating Models is here, again just a simple training script and the corresponding Jupyter notebook
- Last but not least, the code for Round 4 - Deploying and Consuming Models is here, where we also have the
conda_dependencies.ymlfiles needed to build the Docker image. And of course, the
input.jsonfile used for invoking the scoring web service (this uses the standard structure for Azure ML Studio, this is why the code in
score.pylooks the way it does)
- The Machine Learning Studio experiments are available in the Azure AI Gallery: Round 1, Round 2, and Round 3. Since Round 4 was all about deploying the experiment as a web service, you can reuse the Round 3 experiment.