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AWS Machine Learning Engineer

Nanodegree Program

Meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights.

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00Days07Hrs49Min00Sec

  • Estimated time
    5 Months

    At 5-10 hours/week

  • Enroll by
    August 17, 2022

    Get access to the classroom immediately upon enrollment

  • Prerequisites
    Basic knowledge of machine learning algorithms and Python programming
In collaboration with
  • AWS

What you will learn

  1. AWS Machine Learning Engineer

    Estimated 5 months to complete

    You’ll master the skills necessary to become a successful ML engineer. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker.

    Prerequisite knowledge

    Basic knowledge of machine learning algorithms and Python programming.

    1. Introduction to Machine Learning

      In this course, you'll start learning about machine learning through high level concepts through AWS SageMaker. You'll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.

    2. Developing Your First ML Workflow

      In this course you will learn how to create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline.

    3. Deep Learning Topics within Computer Vision and NLP

      In this course you will learn how to train, finetune, and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and which tools are used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.

    4. Operationalizing Machine Learning Projects on SageMaker

      This course covers advanced topics related to deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs. You will also learn how to deploy projects that can handle high traffic and how to work with especially large datasets.

    5. CAPSTONE PROJECT: Inventory Monitoring at Distribution Centers

      Distribution centers often use robots to move objects as a part of their operations. Objects are carried in bins where each bin can contain multiple objects. In this project, students will have to build a model that can count the number of objects in each bin. A system like this can be used to track inventory and make sure that delivery consignments have the correct number of items. To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio.

    All our programs include:

    • Real-world projects from industry experts

      With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.

    • Technical mentor support

      Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.

    • Career services

      You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.

    • Flexible learning program

      Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

    Program offerings

    • Class content

      • Real-world projects
      • Project reviews
      • Project feedback from experienced reviewers
    • Student services

      • Technical mentor support
      • Student community
    • Career services

      • Github review
      • LinkedIn profile optimization

    Succeed with personalized services.

    We provide services customized for your needs at every step of your learning journey to ensure your success.

    Get timely feedback on your projects.

    • Personalized feedback
    • Unlimited submissions and feedback loops
    • Practical tips and industry best practices
    • Additional suggested resources to improve
    • 1,400+

      project reviewers

    • 2.7M

      projects reviewed

    • 88/100

      reviewer rating

    • 1.1 hours

      avg project review turnaround time

    Mentors available to answer your questions.

    • Support for all your technical questions
    • Questions answered quickly by our team of technical mentors
    • 1,400+

      technical mentors

    • 0.85 hours

      median response time

    Learn with the best.

    Learn with the best.

    • Matt Maybeno

      Principal Software Engineer - Data Science and Machine Learning

      Matt Maybeno is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.

    • 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.

    • 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.

    • Soham Chatterjee

      GRADUATE STUDENT AT THE NANYANG TECHNOLOGICAL UNIVERSITY

      Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

    • Bradford Tuckfield

      Independent Consultant

      Bradford does independent consulting for machine learning projects related to manufacturing, law, pharmaceutical operations, and other fields. He also writes technical books about programming, algorithms, and data science.

    AWS Machine Learning Engineer

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      Best Value
    • Learn

      Amazon SageMaker best practices, including new model design and deployment features and case studies to which they can be applied.
    • Average Time

      On average, successful students take 5 months to complete this program.
    • Benefits include

      • Real-world projects from industry experts
      • Technical mentor support
      • Career services

    Program details

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    AWS Machine Learning Engineer

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