In Learning Machine Learning – part 1, we covered AWS and GCP tutorials on machine learning within each of their clouds. In part 2 we cover Microsoft tutorial(s) on machine learning in Azure.
I found Machine Learning Jump Start in Microsoft Visual Academy with instructors, Buck Woody and Seayoung Rhee. This is a series of 4 video tutorials on Azure ML Studio. ML studio seems similar to AWS SageMaker as it’s a framework to perform machine learning.
Azure (and probably AWS & GCP) have a number of methods to perform machine learning. ML studio happens to be the one that I found but there are many others worth examining.
Azure’s ML Studio tutorial videos were a better than AWS but not as good as GCP IMHO for learning machine learning. There are four videos in the series. I watched the first (~45 minutes), the second (~45 minutes) and most of the third (only 25 of ~45 minutes).
Video 1 Concepts and setting up a ML Studio account
In the first video, the instructors took a long time to get going and then when got someplace interesting, it was all play acting (human as a machine learner) to teach concepts.
The tutorials do distinguish between Supervised learning and Unsupervised learning. Both of which can apply to prediction or classification types of problems or outcomes. These are discussed as classic machine learning characteristics.
In the last 1/3 of the first video they discuss Azure ML Studio. It provides a common place to work and collaborate across team members. It also provides a graphical approach to machine learning. ML Studio also supports a programmable API, but I never got to that section in my viewing.
Some Azure ML Studio strengths:
- It provides industry recognized data sets and data science algorithms that can be used as a black box, such as recommendation engines.
- It allows you to publish and consume machine learning solutions.
On the Azure portal there’s a machine learning studio icon (it’s now buried under the 100+ services link, in the AI + Machine Learning section). You use this to create a new ML studio workspace.
Inside a workspace you can use Azure ML studio services. In the workspace you can review all your experiments (these are algorithms or predictive models being worked).
In the Experiments page you can create a new experiment which is sort of a graphical workflow of the machine learning task.
There you will find a list of Azure sample data sets and sample algorithms that can be used in your experiment. The first video didn’t go into much detail on any of this other than showing you how to get started and create a ML studio workspace.
Video 2 how to use ML studio
Video 2 takes your ML studio workspace and runs a rudimentary experiment with it. In this video they walk you through selecting a data set, selecting algorithms to use and how to connect them into a machine learning workflow.
Creating an ML Studio experiment is almost like flowcharting your workflow. You select the data you want and drop it into the workflow. Next select an extraction engine you want to use and drop it into the work flow and connect it to the data. Then. you identify what you want to do with the data (like training) and drop that algorithm into the workflow and so on. In the end you have defined a sequence of actions to perform on data.
In their example, dataset they use a user movie ratings dataset. They connect this to a bayesian learning model and to a IMDB database to extract movie titles. The tutorial experiment is a movie recommendation engine.Although it wasn’t a neural net many of the same techniques apply.
ML Studio uses an intuitive graphical approach to defining a machine learning workflow.
Video 3 publishing your ML Studio web service
Video 3 shows you how to publish (on Azure’s Marketplace) the recommendation engine created in video 2 as an OData web service.
I stopped watching the 3rd video after about 25 minutes as it was setting up various aspect of the OData web service to be deployed on Azure marketplace.
Using Azure ML studio seemed pretty straightforward. But it was much more data science/data analytics activity than neural network training.
The Azure MVA ML Studio tutorial was created in 2014 so some of the concepts are a bit dated but most still apply.
Looking today on the Azure Portal, I was still able to find the ML studio workspaces under one of the 10 AI + Machine Learning services. Again I would have to say the GCP tutorial was a better fit for what I wanted which was how do I create a neural net and get it trained.
Other ML approaches under Azure
There are other Azure approaches to machine learning and tutorials that support them. For example, there’s a quick start tutorial to understand how to use Python and Jupyter notebooks under Azure, which is probably closer to the neural net training in GCP.
I found myself skipping ahead a lot in video 1 as it was mainly about concepts and not much technical detail. Video 2 was a good intro into ML studio and Video 3 showed you how to publish a ML studio web service in Azure but it was more details than I wanted to know. I never got to video 4, which probably talked about ML Studio’s programable API.
If I had to do it over again, I probably would have viewed the quick start tutorial with Python and Jupyter notebooks, which sounded more like the GCP tutorials in the part 1 post.
On the other hand, Azure ML Studio tutorials supplied a good complement to the GCP tutorial, as a different (more graphical) way to do ML. It would probably be worthwhile to view before taking the AWS Sagemaker tutorials as it’s a bit higher level and quicker introduction into the workflow of AI and machine learning.
Comments?
Picture credit(s): Screen shots of Videos 1, 2 and 3 in the MVA series, (c) Microsoft
There’s been a lot of talk on the extendability of current AI this past week and it appears that while we may have a good deal of runway left on the machine learning/deep learning/pattern recognition, there’s something ahead that we don’t understand.
Where’s HAL?
There’s a case to be made that all mammalian intelligence is based on hierarchies of pattern recognition capabilities.
Most babies understand their parents language(s) and learn to crawl within months after birth. But they haven’t listened to thousands of hours of people talking or crawled thousands of miles. And yet, deep learning requires even more learning sets in order to label language properly or learning how to crawl on four appendages. And of course, understanding language and speaking it are two different capabilities. Ditto for crawling and walking.
