AWS Machine Learning Engineer Nanodegree v2.0.21

Nanodegree key: nd189

Version: 2.0.21

Locale: en-us

The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to.

Content

Part 01 : Welcome to AWS Machine Learning Engineer Nanodegree

Module 01: Welcome!
- Lesson 01: 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.
- Lesson 02: Getting Help
  You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Part 02 : Introduction to Machine Learning

In this course, you'll start learning what machine learning is by being introduced to the 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.

Module 01: Course 1 Refresh [Enter Title Here]
- Lesson 01: Introduction to Machine Learning
  Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.
- Lesson 02: 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.
- Lesson 03: 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.
- Lesson 04: 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.
- Lesson 05: 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
- Lesson 06: 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!
  
  Project Description - Predict Bike Sharing Demand with AutoGluon
  
  Project Rubric - Predict Bike Sharing Demand with AutoGluon

Part 03 : Developing your First ML Workflow

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.

Module 01: Course 2 Refresh [Enter Title Here]
- Lesson 01: Introduction to Developing ML Workflows
  This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.
- Lesson 02: SageMaker Essentials
  This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.
- Lesson 03: Designing Your First Workflow
  This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.
- Lesson 04: 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 05: 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.
  
  Project Description - Build a ML Workflow For Scones Unlimited On Amazon SageMaker
  
  Project Rubric - Build a ML Workflow For Scones Unlimited On Amazon SageMaker

Part 04 : Deep Learning Topics with Computer Vision & 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 the tools 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.

Module 01: Course 3 Refresh [Enter Title Here]
- Lesson 01: 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.
- Lesson 02: Introduction to Deep Learning
  In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.
- Lesson 03: Common Model Architecture Types and Fine-Tuning
  In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.
- Lesson 04: 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.
- Lesson 05: 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.
  
  Project Description - Image Classification using AWS SageMaker
  
  Project Rubric - Image Classification using AWS SageMaker

Part 05 : Operationalizing Machine Learning on SageMaker

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.

Module 01: Course 4 Refresh [Enter Title Here]
- Lesson 01: 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 02: 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 03: 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 04: 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 05: 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 06: 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.
  
  Project Description - Operationalizing an AWS ML Project
  
  Project Rubric - Operationalizing an AWS ML Project

Part 06 : Capstone Build Your Own Machine Learning Portfolio

Module 01: Capstone Project
- Lesson 01: 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.
- Lesson 02: Capstone Proposal
  Submit a Project Proposal for the Project that you selected.
  
  Project Description - Capstone Proposal
  
  Project Rubric - Capstone Proposal
  - Concept 01: Capstone Proposal
- Lesson 03: Machine Learning Capstone
  Once your project proposal is approved now it is time to actually build out the project.
  
  Project Description - Machine Learning Capstone
  
  Project Rubric - Machine Learning Capstone
  - Concept 01: Capstone Project
  - Concept 02: Course - AWS Sign In and Costs

Part 07 : Congratulations!

Congratulations on finishing your program!

Module 01: Congratulations!
- Lesson 01: Congratulations!
  Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.

**Part 08 (Career):** Career Services

Module 01: Career Services
- Lesson 01: Optimize Your GitHub Profile
  Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile
- Lesson 02: Take 30 Min to Improve your LinkedIn
  Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile