22-25 April 2026 
SQLBits 2022
Docker & Kubernetes for the Data Scientist
Deployment == Return on investment. This session looks to show you how to do that for Machine Learning.
Deploying Machine Learning models is known as the hardest problem in Data Science. Too many models live and die on a developers machine. We need a way to deploy our models in a repeatable way. In this session we will look at the basics and the history of Docker. We will build a Machine Learning model in Python, serialise it and containerise it.
Docker is great for packaging our applications, but we need somewhere to run it. For this we will use Kubernetes. Again we will look at the basics and history of K8s (how the kool kids write Kubernetes). We will then get our docker container running our model live and in to production.
Too few machine learning developers can deploy models, lets change this by running through all the examples together in this session.
feedback link: https://sqlb.it/?7311
Docker is great for packaging our applications, but we need somewhere to run it. For this we will use Kubernetes. Again we will look at the basics and history of K8s (how the kool kids write Kubernetes). We will then get our docker container running our model live and in to production.
Too few machine learning developers can deploy models, lets change this by running through all the examples together in this session.
feedback link: https://sqlb.it/?7311
Speakers
Terry McCann's previous sessions
Docker & Kubernetes for the Data Scientist
Deployment == Return on investment. This session looks to show you how to do that for Machine Learning.
Machine Learning in Azure Synapse
There is a lot of content available on Synapse for Data Engineering, but what about Machine Learning? In this session we will look at how to integrate a SparkML model in Synapse.
Rapid Requirements: Introducing the Machine Learning Canvas
In this session, we will introduce the Advancing Analytics Machine Learning Canvas and how it can be used to capture requirements for Machine Learning Projects.
Machine Learning in Azure Databricks
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
Deploy ML models faster with Data Science DevOps
In this session I will show you how to apply DevOps practices to speed up your development cycle and ensure that you have robust deployable models. We will focus on the Azure cloud platform in particular, however this is applicable to other platforms
Enhancing relational models with graph in SQL Server 2017
This session explores SQL Server 2017's Graph processing to better understand interconnectivity and behaviour in your data.