Deploying Scalable Machine Learning for Data Science
Offered By: LinkedIn Learning
Course Description
Overview
Learn how to use design patterns for scalable architecture and tools such as services and containers to deploy machine learning at scale.
Syllabus
Introduction
- Scaling ML models
- What you should know
- Building and running ML models for data scientists
- Building and deploying ML models for production use
- Definition of scaling ML for production
- Overview of tools and techniques for scalable ML
- Horizontal vs. vertical scaling
- Running models as services
- APIs for ML model services
- Load balancing and clusters of servers
- Scaling horizontally with containers
- Services encapsulate ML models
- Using Plumber to create APIs for R programs
- Using Flask to create APIs for Python programs
- Best practices for API design for ML services
- Containers bundle ML model components
- Introduction to Docker
- Building Docker images with Dockerfiles
- Example Docker build process
- Using Docker registries to manage images
- Running services in clusters
- Introduction to Kubernetes
- Creating a Kubernetes cluster
- Deploying containers in a Kubernetes cluster
- Scaling up a Kubernetes cluster
- Autoscaling a Kubernetes cluster
- Monitoring service performance
- Service performance data
- Docker container monitoring
- Kubernetes monitoring
- Best practices for scaling ML
- Next steps
Taught by
Dan Sullivan
Related Courses
Introduction to Cloud Infrastructure TechnologiesLinux Foundation via edX Scalable Microservices with Kubernetes
Google via Udacity Introduction to Kubernetes
Linux Foundation via edX Architecting Distributed Cloud Applications
Microsoft via edX IBM Cloud: Deploying Microservices with Kubernetes
IBM via Coursera