Hello and welcome to this full day workshop on deploying machine learning models in Azure. You could use any cloud provider for what we are looking. There is lots in this course that is directly transferable to AWS or Google. You can also run this on-premises, however that misses a lot of what we are trying to create with the cloud.
I have been writing an ongoing blog series about the content in this blog. I will add more to it based on the content in this session. You can find it all here:
Pick up a book on Machine learning and it will explain the process for machine learning, many citing CRISP-DM as the ideal process. CRISP-DM is an iterative approach to Data Mining. It starts with business understanding the flows to data understanding, data preparation, modelling, evaluation, then either loops back around or is deployed.
How it is deployed, well no one ever tells you that! Well, I want to talk about it!
In this full-day session we will build a series of basic models and promote them into production. This will be an interactive session, make sure you have your laptop with you. As we go through the day we will talk about the following:
Developing a Machine Learning Engineering environment Develop multiple basic machine learning models Deploy multiple basic machine learning models Develop an architecture capable of supporting and deploying any machine learning language Sounds awesome right? My intention is to show you a method for deploying machine learning models.
We will do this by looking at the following tech stack:
- Microsoft Azure – A Cloud environment to deploy to. (All the tech we are using will work on a platform of your choice)
- Python – To build our models
- Docker – A container to run our models
- Kubernetes – A Container runtime environment to handle the load balancing of our models.
- Azure Service Bus – A stream service for our models
- PowerBI – A reporting tool to visualise the usage of our models.
- We will use a composition of other languages as we go.
All the scripts we will use will be available to GitHub for you to follow along.
At the end of the day we will have built a simple model and deployed it. You will take away a tried and tested architecture for deploying a model in to production. I will demo a method for deploying changes to your model using DevOps.
- M0 - Title Intro Agenda
- M1 - Intro to Machine Learning with Python
- M2 - Creating our first Machine learning model.
- M3 - Python Serailisation.
- M4 - Creating REST APIs
- M5 - An introduction to Docker
- M6 - An Intorduction to Kubernetes
- M7 - DevOps for Machine Learning
- M8 - Extending the design - The Rendezvous pattern
If we somehow make it through the day and we still have a load oftime left over. I will demo how we can serialise a model inside SQL Server and use a database to productionise a model
This session is designed to be a hands on workshop. You will get a mixture of Theory and real world solutions. To back this up we have a series of labs.
- Lab 1 - Creating a Python Model
- Lab 2 - The basics of serialisation in Python
- Lab 3 - Adding Flask to our model to expose an API
- Lab 4 - Hello World! in Docker
- Lab 5 - Deploying a web app in to Docker
- Lab 6 - Deploying our model in to Docker
- Lab 7 - Deploying Azure Container Reg
- Lab 8 - Deploying our model in Kubernetes
- Slides - You will find the latest version of all the slides located here.
- Labs - All the labs we will run through during this session.
- Code Examples - You will see as we go through the session a lot of code in the slides. Rather than copying this from the slides, all content is here too.
- Images - All the images used as documentation in labs.
These labs require tools most Azure developers have on their development machines. They do need to be the latest edition for some of the new features to work.
As a minimum delegates should have:
- A laptop.
- An Azure subscription with available credit. https://azure.microsoft.com/en-gb/offers/ms-azr-0044p/
- A modern internet browser.
For delegates to get the most out of the day and get involved in advanced areas, we also recommend having the following tools installed:
- Docker for your OS - https://www.docker.com/
- Python (Anaconda preferred) - https://www.anaconda.com/download/
- Azure CLI - https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest
- Kubectl - https://docs.microsoft.com/en-us/cli/azure/acs/kubernetes?view=azure-cli-latest
That should be enough to follow along with all the demos or to take the labs home to do in your own time.
There are business cards for all speakers on the desk at the front.
Terry is a principal consultant for adatis delivering some of the most advanced solutions in Azure in the UK. Microsoft Data Platform MVP. Terry holds a Data Science Master's degree, is the organizer of the Data Science Exeter user group, frequent speaker at conferences across the world. He has a particular interest in Machine Learning, DevOps, DataOps and Python. Feel free to ask me about advanced Machine learning deployments.
Be sure to check out his upcoming talks on Machine Learning.
You can contact Terry here: tpm@adatis.co.uk or via @SQLShark on Twitter
As we talk about interesting links and examples we will collect these here for future reference.
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Azure Model Management - Now marked for retirement