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AI Engineering Bootcamp: Learn AWS SageMaker with Patrik Szepesi
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Course Introduction
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Setting Up Our AWS Account
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Set Up IAM Roles + Best Practices
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AWS Security Best Practices
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Set Up AWS SageMaker Domain
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UI Domain Change
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Setting Up SageMaker Environment
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SageMaker Studio and Pricing
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Setup: SageMaker Server + PyTorch
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HuggingFace Models, Sentiment Analysis, and AutoScaling
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Get Dataset for Multiclass Text Classification
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Creating Our AWS S3 Bucket
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Uploading Our Training Data to S3
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Exploratory Data Analysis - Part 1
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Exploratory Data Analysis - Part 2
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Data Visualization and Best Practices
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Setting Up Our Training Job Notebook + Reasons to Use SageMaker
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Python Script for HuggingFace Estimator
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Creating Our Optional Experiment Notebook - Part 1
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Creating Our Optional Experiment Notebook - Part 2
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Encoding Categorical Labels to Numeric Values
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Understanding the Tokenization Vocabulary
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Encoding Tokens
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Practical Example of Tokenization and Encoding
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Creating Our Dataset Loader Class
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Setting Pytorch DataLoader
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Which Path Will You Take?
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DistilBert vs. Bert Differences
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Embeddings In A Continuous Vector Space
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Introduction To Positional Encodings
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Positional Encodings - Part 1
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Positional Encodings - Part 2 (Even and Odd Indices)
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Why Use Sine and Cosine Functions
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Understanding the Nature of Sine and Cosine Functions
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Visualizing Positional Encodings in Sine and Cosine Graphs
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Solving the Equations to Get the Values for Positional Encodings
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Introduction to Attention Mechanism
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Query, Key and Value Matrix
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Getting Started with Our Step by Step Attention Calculation
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Calculating Key Vectors
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Query Matrix Introduction
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Calculating Raw Attention Scores
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Understanding the Mathematics Behind Dot Products and Vector Alignment
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Visualizing Raw Attention Scores in 2D
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Converting Raw Attention Scores to Probability Distributions with Softmax
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Normalization
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Understanding the Value Matrix and Value Vector
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Calculating the Final Context Aware Rich Representation for the Word "River"
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Understanding the Output
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Understanding Multi Head Attention
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Multi Head Attention Example and Subsequent Layers
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Masked Language Learning
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Exercise: Imposter Syndrome
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Creating Our Custom Model Architecture with PyTorch
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Adding the Dropout, Linear Layer, and ReLU to Our Model
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Creating Our Accuracy Function
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Creating Our Train Function
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Finishing Our Train Function
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Setting Up the Validation Function
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Passing Parameters In SageMaker
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Setting Up Model Parameters For Training
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Understanding The Mathematics Behind Cross Entropy Loss
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Finishing Our Script.py File
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Quota Increase
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Starting Our Training Job
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Debugging Our Training Job With AWS CloudWatch
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Analyzing Our Training Job Results
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Creating Our Inference Script For Our PyTorch Model
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Finishing Our PyTorch Inference Script
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Setting Up Our Deployment
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Deploying Our Model To A SageMaker Endpoint
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Introduction to Endpoint Load Testing
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Creating Our Test Data for Load Testing
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Upload Testing Data to S3
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Creating Our Model for Load Testing
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Starting Our Load Test Job
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Analyze Load Test Results
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Deploying Our Endpoint
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Creating Lambda Function to Call Our Endpoint
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Setting Up Our AWS API Gateway
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Testing Our Model with Postman, API Gateway and Lambda
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Cleaning Up Resources
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Thank You!
