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Developing your First ML Workflow

Course

This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.

This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.

Built in collaboration with

AWS

Intermediate

4 weeks

Real-world Projects

Completion Certificate

Last Updated February 26, 2024

Skills you'll learn:
AWS lambda • Sagemaker processing • Sagemaker batch transform jobs • Sagemaker training jobs
Prerequisites:
Machine learning fluency • AWS familiarity • API proficiency

Course Lessons

Lesson 1

Introduction to Developing ML Workflows

This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.

Lesson 2

SageMaker Essentials

This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.

Lesson 3

Designing Your First Workflow

This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.

Lesson 4

Monitoring a ML Workflow

This lesson will go over monitoring a machine learning workflow and some useful services within AWS to help you monitoring the healthy of data and machine learning models.

Lesson 5 • Project

Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker

In the project, you will build and ship an image classification model with AWS SageMaker for Scones Unlimited, a scone-delivery-focused logistic company.

Taught By The Best

Photo of Charles Landau

Charles Landau

Technical Lead, AI/ML - Guidehouse

Charles holds a MPA from George Washington University, where he focused on econometrics and regulatory policy, and holds a BA from Boston University. At Guidehouse, he supports data scientists and developers working on internal and client-facing ML platforms.

Photo of Joseph Nicolls

Joseph Nicolls

Senior Machine Learning Engineer - Blue Hexagon

Joseph Nicolls is a senior machine learning scientist at Blue Hexagon. With a major in Biomedical Computation from Stanford University, he currently utilizes machine learning to build malware-detecting solutions at Blue Hexagon.

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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

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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.

  • Get help from subject matter experts

  • Learn industry best practices

  • Gain valuable insights and improve your skills

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4 Months

Average time to complete a Nanodegree program

*Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.

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Developing your First ML Workflow

Month-To-Month


  • Unlimited access to our top-rated courses
  • Real-world projects
  • Personalized project reviews
  • Program certificates
  • Proven career outcomes

4 Months

Average time to complete a Nanodegree program

  • All the same great benefits in our month-to-month plan
  • Most cost-effective way to acquire a new set of skills
Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.