YoVDO

Deploying Scalable Machine Learning for Data Science

Offered By: LinkedIn Learning

Tags

Machine Learning Courses Data Science Courses Docker Courses Flask Courses Kubernetes Courses Microservices Courses APIs Courses Scalable Architectures Courses Containerization Courses

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
1. The Need to Scale ML Models
  • 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
2. Design Patterns for Scalable ML Applications
  • Horizontal vs. vertical scaling
  • Running models as services
  • APIs for ML model services
  • Load balancing and clusters of servers
  • Scaling horizontally with containers
3. Deploying ML Models as Services
  • 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
4. Running ML Services in Containers
  • Containers bundle ML model components
  • Introduction to Docker
  • Building Docker images with Dockerfiles
  • Example Docker build process
  • Using Docker registries to manage images
5. Scaling ML Services with Kubernetes
  • 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
6. ML Services in Production
  • Monitoring service performance
  • Service performance data
  • Docker container monitoring
  • Kubernetes monitoring
Conclusion
  • Best practices for scaling ML
  • Next steps

Taught by

Dan Sullivan

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