Data Engineering with AWS Technology-About the Course
Meet your Course Instructor: Noah Gift
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Meet your Supporting Instructor: Alfredo Deza
Course Structure and Discussion Etiquette
Getting Started and Course Gotchas
Data Engineering with AWS Technology-Getting Started with AWS Machine Learning Technology
Key Terms
Welcome to AWS Academy Machine Learning Foundations
Studio Lab Examples
Using Sagemaker Studio Lab
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Getting Started with AWS CloudShell
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Advantages of Using Cloud Developer Workspaces
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Prototyping AI APIs in CloudShell
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Cloud9 with AWS Codewhisperer AI Pair Programming Tool
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AWS Academy Onboard (Optional)
Lesson Reflection
Data Engineering with AWS Technology-Creating Data Repositories for Machine Learning
Key Terms
Developing AWS Storage Solutions
Introduction to Data Storage
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Determining the Correct Storage Medium
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Working with Amazon S3
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Data Lakes with Amazon S3
Lesson Reflection
Data Engineering with AWS Technology-Identifying and Implementing Data Ingestion and Transformation Solutions
Key Terms
Batch vs. Streaming Job Styles
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Introduction to Data Ingestion and Processing Pipelines
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Working with AWS Batch
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Working with AWS Step Functions
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Transforming Data in Transit
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Handling Map Reduce for Machine Learning
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Working with EMR Serverless
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Interactive Marco Polo Pipeline Programming Challenge
Lesson Reflection
Exploratory Data Analysis with AWS Technology-Sanitizing and Preparing Data for Modeling
Key Terms
AWS Academy Introduction to Machine Learning
Cleaning Up Data
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Scaling Data
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Labeling Data
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AWS Resources for Exploratory Data Analysis
Lesson Reflection
Exploratory Data Analysis with AWS Technology-Performing Feature Engineering
Key Terms
Identifying and Extracting Features
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Feature Engineering Concepts
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Feature engineering with scikit-learn on Databricks
Lesson Reflection
Exploratory Data Analysis with AWS Technology-Analyzing and Visualizing Data for Machine Learning
Key Terms
Graphing Data
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Clustering Data
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Lesson Reflection
Modeling with AWS Technology-Selecting the Appropriate Model(s) for a Given Machine Learning Problem
Key Terms
When to Use Machine Learning?
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Supervised vs. Unsupervised Machine Learning
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Introduction to Implementing a Machine Learning Pipeline with Amazon SageMaker
Selecting a Machine Learning Solution
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Lesson Reflection
Modeling with AWS Technology-Training Machine Learning Models
Key Terms
Introducing Forecasting on Sagemaker
Selecting a Machine Learning Model
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Modeling Demo with Sagemaker Canvas
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Using Train, Test and Split
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Solving Optimization Problems
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Selecting GPU vs. CPU
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Neural Network Architecture
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Interactive Gradient Descent
Lesson Reflection
Modeling with AWS Technology-Evaluating Machine Learning Problems
Key Terms
Introducing Computer Vision
Overfitting vs. Underfitting
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Selecting Metrics
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Comparing Models using Experiment Tracking
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More Practice: Train an Image Classification Model with PyTorch
Lesson Reflection
MLOps with AWS Technology-Building Machine Learning Solutions for Performance, Availability, Scalability, Resilience and Fault
Key Terms
Introducing Natural Language Processing
Monitoring and Logging
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Interactive Python Logging
Multiple Regions
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Reproducible Workflows
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AWS-Flavored DevOps
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Lesson Reflection
MLOps with AWS Technology-Recommending and Implementing Appropriate Machine Learning Services
Key Terms
Reviewing Compute Choices
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Provisioning EC2
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Provisioning EBS
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AWS AI ML Services
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More Practice: Deploy a Hugging Face Pre-trained Model to Amazon SageMaker
Lesson Reflection
MLOps with AWS Technology-Deploying and Operationalizing Secure Machine Learning Solutions
Key Terms
Principle of Least Privilege AWS Lambda
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Integrated Security
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Overview of Sagemaker Studio Workflow
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Model Predictions with Sagemaker Canvas
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Data Drift and Model Monitoring
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Running PyTorch with AWS App Runner
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More Practice: Deploy Models for Inference
AWS Certified Machine Learning – Specialty
External Lab: MLOps Template GitHub
Lesson Reflection
Machine Learning Certifications-Azure AI Fundamentals and other Azure Certifications
Key Terms
Introduction to Azure Certifications
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Learning Resources for Azure Certifications
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Microsoft Learning Paths and Study Notes
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Creating an Azure ML Workspace
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Creating an Azure Auto ML Job
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Lesson Reflection
Machine Learning Certifications-Introductory Azure ML and MLOps Concepts
Key Terms
Introductory Azure ML and MLOps Concepts
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Prerequisite Technology
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Real Time and Batch Deployment
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Azure Open Datasets
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Exploring Open Datasets SDK
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Lesson Reflection
Machine Learning Certifications-More Advanced Azure ML and MLOps Concepts
Key Terms
More Advanced Azure ML and MLOps Concepts
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Exploring Azure ML Command Line
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Triggering Azure ML with GitHub
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Using Hyperparameters
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Train a Model using the Python SDK
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Lesson Reflection
Next Steps
