This intensive two-day hands-on course is designed to provide working data scientists and other technology professionals with a comprehensive introduction to Kubernetes and its use in data intensive applications. Attendees will leave with a clear understanding of Kubernetes data processing application design and architecture. Students will gain hands on experience with Kubernetes manifest coding from pod and job basics all the way through advanced topics such as stateful services, volumes, auto scaling and configuration. Skills developed include Pod specification and common analytics and data processing pod design patterns, batch and cron jobs, Spark scheduling, ML operations and more. Best practices are covered in class and through the hands-on lab exercises accompanying each module. Upon completion of the course, attendees will have the skills and information necessary to begin creating effective application manifests for sophisticated cloud native data science applications.
Available for Instructor-Led (ILT) in-person/onsite training or Virtual Instructor-Led training (VILT) delivery; Open Enrollment options may be available.
Who Should Attend
Data Scientists, Big Data Practitioners, Data Engineers, Machine Learning Developers
What Attendees will learn
This course is designed to provide data scientists with a comprehensive introduction to Kubernetes. Upon completion of the course, attendees will have the skills and information necessary to begin creating effective application manifests for sophisticated cloud native data science applications. Learning modules include:
- Kubernetes architecture and data oriented use cases
- Controlling workloads on Kubernetes
- Data driven applications
- Spark use cases
- Machine learning use cases
Students should have taken “Docker Foundation” course or have equivalent knowledge. The “Kubernetes Foundation” course is highly recommended as a prerequisite but not required. Each student must have the ability to run a 64 bit virtual machine (provided) and have good internet access.