Docker & Kubernetes for the Data Scientist

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:

Starts: 17:10 10th Mar 2022
Ends: 18:00 10th Mar 2022


Short Description
- Deployment == Return on investment. This session looks to show you how to do that for Machine Learning.


Terry McCann


AWSAzureGCPDataOpsIntelligence and AnalyticsDeploymentDevelopingAI and Data ScienceBig Data AnalyticsAdvancedHow

The SQL Bits Story

SQLBits was formed in 2007 by a group of volunteers who were passionate about the SQL Server product suite and wanted to provide much-needed community-driven education to the data community.

As one of the largest data platform conferences in the world, we offer more opportunities to a wider audience.

15 Years

We’ve grown and expanded a lot since 2007.

2500 Participants

SQLBits is the best place to meet fellow data professionals.

82 Countries

We welcome data professionals from all over the globe.

1140 recorded sessions

All the live sessions are recorded and offered for free, year round.

Experience the SQLBits Conference


Want to be part of the SQLBits community?

Attend the London conference in-person or virtually on 

March 8-12, 2022 at ExCel London, UK.