This article we will do a light touch on Cosmos DB; specifically the Mongo API from Cosmos DB and using that API from Mongo Engine. I think one of the great things about Cosmos DB’s Mongo API is that I simply swap out my connection strings and guess what; it works! This means not only can I use Mongo Engine, but I can use PyMongo or any other framework for any language that connects to Mongo.
So Jupyter is a great tool for experimental science. Running a jupyter notebook though can be tricky; especially if you want to maintain all of the data that is stored in it. I have seen many strategies; but I have come up with one that I like best of all. It is based on my “Micro Services for Data Science” strategy. By using decoupled data and compute we can literally thrash our Jupyter notebook and all of our data and notebooks still live. So why not put it in a self healing orchestrater and deploy via Kubernetes :D.
This article we will do a bit of a review of the technology stack required to enable this as well as the logistics behind setting it all up and operating against it. The solution uses Azure Container Services, Docker, Kubernetes, Azure Storage, Jupyter, Tensor Flow and Tensorboard. WOW! That is a lot of technology; so we won’t do a deep dive how to but rather some pointers on how to get provisioned and then the high level process on how to use it.
So today we are going to do something really awesome. Operationalize Keras with Azure Machine Learning. Why in the world would we want to do this? Well we can configure Deep Neural Nets and train them on GPU. In fact, in this article, we will train a 2 depth neural network which outputs a linear prediction of energy efficiency of buildings and then operationalize that GPU trained network on Azure Machine Learning for production API usage.
So today I have decided to actually begin a write up on where I’m headed. Its taken a long while to come up with what my next adventure after food trucks was going to be, especially with all of the things you can do with embedded technologies and machine learning. So here it is, a self driving race car.
In this article I aim to lay out the high level plan of attack for what my team and I are building and some of the direction on where we are headed.
So I just completed an incredible project with Brain Thermal Tunnel Genix, where I learned so much about pattern recognition, machine learning and taking research and algorithms and pushing those into a production environment where it can be integrated into a real product. Today’s article takes those lessons and provides a sample on how to perform complex modelling and operationalize it in the cloud. The accompanying Gallery Example can be found here.