AWS Machine Learning – Udacity Nanodegree Course

Course Outline

Course 1: Introduction to Machine Learning

LESSON ONE: Exploratory Data Analysis

• Use AWS SageMaker Studio to access datasets from S3 and perform data analysis functions using AWS tools
• Perform data analysis and feature engineering with Data Wrangler
• Perform data analysis and feature engineering with Pandas in SageMaker Studio
• Label new data for a dataset with SageMaker ground truth

LESSON TWO Machine Learning Concepts

• Design a domain, model, and data outline for a case study
• Build a ML lifecycle and apply it to a dataset
• Differentiate between supervised and unsupervised models and apply them to an appropriate dataset
• Differentiate between regression and classification methods and apply them to an appropriate dataset

LESSON THREE: Model Deployment Workflow

• Load new dataset, create 3 data set types, and identify features/values in SageMaker
• Clean or create new features from a dataset
• Train (fit) a regression/classification model using scikit learn
• Evaluate a trained model using methods like mse, rmse, r2, accuracy, f1, and precision
• Tune a model’s hyper parameters to achieve a better result

LESSON FOUR: Algorithms and Tools

• Train, test, and optimise a linear model, tree-based model,
  XGBoost model, and AutoGluon Tabular prediction model
• Create a model using SageMaker Jumpstart

Course 2: Developing Your First ML Workflow

LESSON ONE Introduction to MLE

• Understand the prerequisites
• Describe key business stakeholders
• Understand the history of MLE
• Describe when to use MLE

LESSON TWO SageMaker Essentials

• Launch training jobs within SageMaker
• Deploy an endpoint that can perform inference on live data
• Evaluate datasets with batch transform jobs.
• Perform custom processing jobs on raw data

LESSON THREE Designing Your Own Workflow

• Create Lambda functions
• Trigger Lambda functions utilizing both the SDK and other
AWS Services
• Design and execute a workflow utilizing State Machines
• Learn about the use cases for SageMaker Pipelines

LESSON FOUR Monitoring a ML Workflow

• Use SageMaker Feature Store to serve and monitor model data
• Configure SageMaker Model Monitor to generate and track metrics about our models
• Use Clarify to explain model predictions and surface biases in models

Course 3: Deep Learning Topics within Computer Vision and NLP

LESSON ONE Introduction to Deep Learning Topics within Computer Vision and NLP

• Understand the need and importance of deep learning
• Learn the history of deep learning and the business stakeholders in a deep learning project
• Learn the tools used by deep learning engineers

LESSON TWO Introduction to Deep Learning

• Understand the workings of artificial neurons and neural networks
• Understand how to set cost functions and optimizers to train neural networks
• Build and train a neural network on an image classification task

LESSON THREE Common Model Architecture Types and Fine-Tuning

• Understand how advanced neural network architectures like convolutional neural networks and transformer-based models work
• Fine tune a pretrained model on a different task
• Understand the important of hyperparameter tuning for
training (and fine-tuning) deep neural networks

LESSON FOUR Deploy Deep Learning Models on SageMaker

• Fine tune models for image and text classification using SageMaker JumpStart
• Debug and profile training jobs using SageMaker Debugger
• Tune hyperparameters when training a model
• Package a model in a Dockerfile for deployment

Course 4: Operationalising Machine Learning Projects on SageMaker

LESSON ONE Manage compute resources in AWS accounts to ensure efficient utilisation

• Keep costs low in AWS machine learning projects
• Use spot instances for efficiency
• Turn off resources when they’re not being used
• Check costs to ensure they remain low

LESSON TWO Train models on large-scale datasets using distributed training

• Perform multi-instance training
• Use distributed data to improve performance
• Create and interpret manifest files
• Choose the best data stores for projects

LESSON THREE Construct pipelines for high throughput, low latency models

• Set up Lambda functions for AWS projects
• Configure endpoints for auto-scaling
• Set up concurrency for Lambda functions
• Create feature stores for data imports

LESSON FOUR Design secure machine learning projects in AWS

• Resolve security issues using IAM settings
• Set up a virtual private cloud for security
• Manage security in SageMaker

CAPSTONE PROJECT: Inventory Monitoring at Distribution Centers

Robots move objects in distribution centers as a part of their operations. In this project, students will develop a model that can count the number of objects within each bin. Objects are carried in bins. It will take students a few days to construct this project, which involves fetching data from a database, preprocessing it, and training a machine learning model using AWS SageMaker and good machine learning engineering practices.

Other Details

• Estimated time 5 Months At 5-10 hours/week
• 5-Month access
  Pay upfront and save an extra 15% ₹114245 ₹97095  for 5-Month access
• Monthly access
  Pay as you go ₹22849 per month