AWS Machine Learning Engineer Nanodegree

An Introduction to Your Nanodegree Program

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Introduction to Machine Learning

Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.

Exploratory Data Analysis

Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.

Machine Learning Concepts

In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.

Model Deployment Workflow

In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.

Algorithms and Tools

In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart

Predict Bike Sharing Demand with AutoGluon

Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!

Introduction to Developing ML Workflows

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

SageMaker Essentials

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

Designing Your First Workflow

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

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.

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.

Introduction to Deep Learning Topics within Computer Vision & NLP

In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.

Introduction to Deep Learning

In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.

Common Model Architecture Types and Fine-Tuning

In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.

Deploy Deep Learning Models on SageMaker

In this lesson, you will learn how to apply all you have learned about deep learning in AWS SageMaker.

Image Classification using AWS SageMaker

In this project, you will use AWS SageMaker to finetune a pretrained model and perform a image classification using profiling, debugging, and hyperparameter tuning.

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.

Machine Learning Engineer Capstone Project

Put your Machine Learning Engineer skills to the test by solving a real-world problem using all that you have learned throughout the program.

Capstone Proposal

Submit a Project Proposal for the Project that you selected.

Machine Learning Capstone

Once your project proposal is approved now it is time to actually build out the project.

Congratulations!

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.

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