A SkillsCast for this session is not available.

Building any production-ready machine learning system is complex. You have to manage services and tools that often don’t play nice with each other. And when they do you have to spend time manually tweaking deployments and hand rolling solutions before a single model can be tested. Worse, these hand-rolled solutions are so tied to your production cluster that it’s impossible to run your code locally making it even harder to spot bugs.

In this workshop, you’ll learn how to leverage Kubernetes to deploying complex workloads in the cloud, on bare metal and locally. You’ll learn how Kubernetes provides a fast iteration cycle, flexible scalability, and a lack of boilerplate which makes it ideal for most of the machine learning experiments.

Please install the following software on your computer:

• - Kubectl
• - Docker
• - Bash (please install Cmder if you're on Windows)
• - Git
• - Python 2.7 and pip
• - A text editor such as Atom

You should also create an account on Docker Hub.

YOU MAY ALSO LIKE:

• Scaling Microservices with Message Queues, Spring Boot and Kubernetes (SkillsCast recorded in November 2018)
• Docker Fundamentals with Matt Saunders (Online Course on 21st - 24th September 2020)
• Advanced Kubernetes Concepts with Jérôme Petazzoni (Online Course on 28th September - 1st October 2020)
• CloudNative eXchange 2020 (Online Conference on 17th - 18th November 2020)
• Ceci n’est pas un canard - Machine Learning and Generative Adversarial Networks (SkillsCast recorded in August 2020)
• Debugging Containers on Kubernetes with "kubectl debug" (SkillsCast recorded in July 2020)