Operationalizing Machine Learning on SageMaker

Course

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.

Built in collaboration with

AWS

Intermediate

4 weeks

Real-world Projects

Completion Certificate

Last Updated February 26, 2024

Skills you'll learn:

Cloud resource allocation • AWS lambda • Distributed model training with sagemaker • Amazon elastic compute cloud

Prerequisites:

No experience required

Course Lessons

Lesson 1

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.

Lesson 2

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.

Lesson 3

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.

Lesson 4

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.

Lesson 5

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.

Lesson 6 • Project

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 The Best

Bradford Tuckfield

Data Scientist and Writer

Bradford Tuckfield is a data scientist and writer. He has worked on applications of data science in a variety of industries. He's the author of Dive Into Algorithms, forthcoming with No Starch Press.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.