- Defining scalability
- Tools and techniques for scalable machine learning
- Architecture design patterns for scalable systems
- Machine learning models as services
- Containerizing models
- Kubernetes for container orchestration
- Monitoring performance
- Best practices for scaling machine learning models
Skill Level Intermediate
- [Instructor] Welcome to this course on scaling machine learning models. In this course, you'll learn about the difference between developing machine learning models and deploying them to a scalable production environment. We'll start by reviewing the model-building process, and discuss the requirements for ensuring that our applications are scalable. We will then examine three layers of a scalable machine learning stack. First is using services to expose machine learning models through APIs. Second, we'll look at containers for deploying models and related code to the production environment.
And then we'll conclude with a look at orchestration tools for managing clusters of servers running our machine learning models. And we'll also discuss the need for monitoring in a production environment. So let's get started on scaling machine learning models.
DevOps Foundations: Containerswith David Linthicum1h 16m Beginner
1. The Need to Scale ML Models
2. Design Patterns for Scalable ML Applications
3. Deploying ML Models as Services
4. Running ML Services in Containers
5. Scaling ML Services with Kubernetes
6. ML Services in Production
Next steps1m 17s
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.