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Machine Learning in Azure: Service versus Studio

This is a more detailed version of my Boy meets Girl talk, created specially for Microsoft Ignite | The Tour Amsterdam 2019. Whereas Boy meets Girl was mostly focused on how to deploy a trained model using either Azure ML Service or ML Studio, here I wanted to create a more in-depth comparison of the two tools. This is what led me to the concept of having multiple rounds, with the audience voting for their favourite tool (truth be told, I think I just wanted another go at delivering something similar to my TypeScript versus CoffeeScript talk ๐Ÿค“).

Once the concept was clear, I spent a significant amount of time just polishing the examples and making sure they’re as exhaustive as possible. And then, of course, another significant amount of time was spent just cutting out things because they didn’t fit with the rest of the story ๐Ÿ™„. All worth it of course, since it allowed me to also have a meaningful conversation with the audience (which was really really really active and involved), answering questions and going into more detail if necessary, instead of just rushing to go through all of the slides.



The resources used during the talk are available on GitHub, below is a quick rundown of what you’ll find there:

  • 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 and conda_dependencies.yml files needed to build the Docker image. And of course, the input.json file used for invoking the scoring web service (this uses the standard structure for Azure ML Studio, this is why the code in looks 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
  • The slides are available on Speaker Deck
  • 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.

Getting Started with Machine Learning Using Azure Machine Learning Studio and Kaggle Competitions

Long title, I know ๐Ÿคซ. It used to be shorter, as some earlier versions of this talk were called ‘Predicting Survivability on the Titanic’, but this time I wanted to experiment a bit and make it real easy for the audience to decide whether or not this would be interesting for them. And so they did.

You see, they wanted to learn more about machine learning. And, the way I see it, the two tools I talked about - Azure Machine Learning Studio and Kaggle Competitions - can help you get started with ML, while also making it fun to do so.

So we proceeded with actually competing live in the Titanic: Machine Learning from Disaster starter competition, downloading the passengers dataset, training a very simple (and overly optimistic) model, a model which crashed and burned when pit against the other participants in the competition ๐Ÿคญ, learning from our mistakes and gradually fixing the issues with the dataset, creating new features, improving the model, and in the end achieving a top 20% score (which, I know, could have been better, but hey, we only had 1 hour to achieve all of this ๐Ÿ˜‰).

Apart from achieving this, I must say I absolutely loved interacting with the audience, answering their questions and discussing the various approaches of parsing data, doing feature engineering, picking the right algorithm, and evaluating a model. It was awesome, and I’m very grateful for that ๐Ÿ˜.



The resources used during the talk are available in the Azure AI Gallery - the Basic Experiment, Feature Engineering, and Binning, whereas the slides are on Speaker Deck.

Boy meets Girl: A Machine Learning Deployment Story

This was a fun talk to write :). Ever since I saw Azure ML Service being announced, I knew I wanted to compare it with ML Studio, a tool with which I had a bit more experience. And so I did.

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.