This course covers advanced topics related to deploying professional machine learning projects on SageMaker. Students will learn how to maximize output while decreasing costs. They will also learn how to deploy projects that can handle high traffic, how to work with especially large datasets, and how to approach security in machine learning AWS applications.
Overview
Syllabus
- Introduction to Operationalizing Machine Learning on SageMaker
- In this introductory lesson, we will give you a course overview of topics and design. We will also introduce what exactly operationalizing machine learning means as well as how it applies.
- Manage compute resources in AWS accounts to ensure efficient utilization
- This lesson is about managing computing resources effectively. We’ll talk about lowering costs and getting more with less.
- Train models on large-scale datasets using distributed training
- This lesson is about training models on large datasets. We’ll talk about distributed models, distributed data, and some skills related to distributed training.
- Construct pipelines for high throughput, low latency models
- This lesson is about high throughput, low latency models. Essentially, this means that we’ll be talking about preparing your projects to deal with high traffic and minimal time delays.
- Design Secure Machine Learning Projects in AWS
- Our final lesson is about security. Security is crucial for all major machine learning projects, so these skills can be very helpful in your career.
- Operationalizing an AWS ML Project
- Your goal in this project will be to use several important tools and features of AWS to adjust, improve, configure, and prepare the model you started with for production-grade deployment.
Taught by
Bradford Tuckfield
