Sagemaker Sessions, Regions, and IAM Roles

AI Engineering: Build, Train, Fine-Tune & Deploy Models with AWS SageMaker

Section 0: Introduction

AI Engineering Bootcamp: Learn AWS SageMaker with Patrik Szepesi (1:35)
Course Setup
Course Introduction Part 1: Build, Train, and Deploy Models with AWS SageMaker (8:42)
Course Introduction Part 2: Fine-Tuning LLMs with QLoRA, AWS, and Open Source (5:19)
Exercise: Meet Your Classmates and Instructor
Course Resources
Understanding Your Video Player
Set Your Learning Streak Goal

Part 1: Build, Train and Deploy Models with AWS SageMaker

Starting Point of Part 1

Section 1: Introduction to AWS, Environment Setup, and Best Practices

Setting Up Our AWS Account (4:31)
Set Up IAM Roles + Best Practices (7:39)
AWS Security Best Practices (7:01)
Set Up AWS SageMaker Domain (2:22)
UI Domain Change (0:42)
Sagemaker Domain Creation Update Part 1 (2:40)
Sagemaker Domain Creation Update Part 2 (3:06)
Sagemaker Notebooks Update (11:58)
Setting Up SageMaker Environment (5:08)
SageMaker Studio and Pricing (8:44)
Let's Have Some Fun (+ More Resources)

Section 2: Possible Resource Limit Errors Before Training and Deployment

Quota Increase (7:35)

Section 3: A Gentle Introduction to HuggingFace in SageMaker

Setup: SageMaker Server + PyTorch (6:08)
HuggingFace Models, Sentiment Analysis, and AutoScaling (18:34)

Section 4: Gathering a Dataset for Our Multiclass Text Classification Project

Get Dataset for Multiclass Text Classification (6:03)
Link to the Dataset
Creating Our AWS S3 Bucket (3:52)
Uploading Our Training Data to S3 (1:26)
Unlimited Updates

Section 5: Exploratory Data Analysis

Exploratory Data Analysis - Part 1 (13:21)
Exploratory Data Analysis - Part 2 (6:07)
Data Visualization and Best Practices (11:08)

Section 6: Setting Up Our Training Notebook

Setting Up Our Training Job Notebook + Reasons to Use SageMaker (18:24)
Python Script for HuggingFace Estimator (13:36)

Section 7: Introduction to Tokenizations and Encodings

Creating Our Optional Experiment Notebook - Part 1 (3:21)
Creating Our Optional Experiment Notebook - Part 2 (4:01)
Encoding Categorical Labels to Numeric Values (13:24)
Understanding the Tokenization Vocabulary (15:05)
Encoding Tokens (10:56)
Practical Example of Tokenization and Encoding (12:48)
Course Check-In

Section 8: Setting Up Data Loading with PyTorch

Creating Our Dataset Loader Class (16:56)
Setting Pytorch DataLoader (15:09)
Implement a New Life System

Section 9: Choose Your Path

Which Path Will You Take? (1:31)

Section 10: Mathematics Behind Large Language Models and Transformers

DistilBert vs. Bert Differences (4:46)
Embeddings In A Continuous Vector Space (7:40)
Introduction To Positional Encodings (5:13)
Positional Encodings - Part 1 (4:14)
Positional Encodings - Part 2 (Even and Odd Indices) (10:10)
Why Use Sine and Cosine Functions (5:08)
Understanding the Nature of Sine and Cosine Functions (9:52)
Visualizing Positional Encodings in Sine and Cosine Graphs (9:24)
Solving the Equations to Get the Values for Positional Encodings (18:07)
Introduction to Attention Mechanism (3:02)
Query, Key and Value Matrix (18:10)
Getting Started with Our Step by Step Attention Calculation (6:53)
Calculating Key Vectors (20:05)
Query Matrix Introduction (10:20)
Calculating Raw Attention Scores (21:24)
Understanding the Mathematics Behind Dot Products and Vector Alignment (13:32)
Visualizing Raw Attention Scores in 2D (5:42)
Converting Raw Attention Scores to Probability Distributions with Softmax (9:16)
Normalization (3:19)
Understanding the Value Matrix and Value Vector (9:07)
Calculating the Final Context Aware Rich Representation for the Word "River" (10:45)
Understanding the Output (1:58)
Understanding Multi Head Attention (11:55)
Multi Head Attention Example and Subsequent Layers (9:51)
Masked Language Learning (2:29)
Exercise: Imposter Syndrome (2:56)

Section 11: Customizing our Model Architecture in PyTorch

Getting Back to SageMaker!
Creating Our Custom Model Architecture with PyTorch (17:14)
Adding the Dropout, Linear Layer, and ReLU to Our Model (15:31)

Section 12: Creating the Accuracy, Training, and Validation Function

Creating Our Accuracy Function (13:04)
Creating Our Train Function (19:08)
Finishing Our Train Function (8:17)
Setting Up the Validation Function (13:40)

Section 13: Optimizer Functions, Model Parameters, Cross Entropy Loss Function

Passing Parameters In SageMaker (4:05)
Setting Up Model Parameters For Training (4:27)
Understanding The Mathematics Behind Cross Entropy Loss (5:39)
Finishing Our Script.py File (6:56)

Section 14: Starting Our Training Job and Monitoring it in AWS CloudWatch

Starting Our Training Job (8:15)
Debugging Our Training Job With AWS CloudWatch (14:17)
Analyzing Our Training Job Results (5:46)
Link to the Finished Trained Model

Section 15: Deploying our Multiclass Text Classification Endpoint in SageMaker

Creating Our Inference Script For Our PyTorch Model (8:34)
Finishing Our PyTorch Inference Script (9:12)
Setting Up Our Deployment (7:30)
Deploying Our Model To A SageMaker Endpoint (8:54)

Section 16: Load Testing Our Machine Learning Model

Introduction to Endpoint Load Testing (4:19)
Information about the next Lesson
Creating Our Test Data for Load Testing (10:02)
Upload Testing Data to S3 (1:03)
Creating Our Model for Load Testing (3:58)
Starting Our Load Test Job (7:14)
Analyze Load Test Results (10:16)

Section 17: Production Grade Deployment of Our Machine Learning Model

Deploying Our Endpoint (3:50)
Creating Lambda Function to Call Our Endpoint (10:26)
Setting Up Our AWS API Gateway (5:27)
Testing Our Model with Postman, API Gateway and Lambda (5:39)

Section 18: Cleaning Up Resources

Finishing Part 1 and Cleaning Up Resources for Part 1 (2:51)

Part 2: Fine-Tuning LLMs with QLoRA, AWS, and Open Source

Starting Point of Part 2

Setting Up AWS Sagemaker Environment

IAM Setup
Creating a SageMaker Domain (2:28)
Logging in to our SageMaker Environment (4:53)
Introduction to JupyterLab (7:37)
Let's Have Some Fun (+ More Resources)

Gathering, Chunking, Tokenizing and Uploading our Dataset

Sagemaker Sessions, Regions, and IAM Roles (7:50)
Examining Our Dataset from HuggingFace (13:29)
Tokenization and Word Embeddings (9:08)
HuggingFace Authentication with Sagemaker (4:21)
Applying the Templating Function to our Dataset (8:43)
Attention Masks and Padding (15:55)
Star Unpacking with Python (4:03)
Chain Iterator, List Constructor and Attention Mask example with Python (10:22)
Understanding Batching (8:11)
Slicing and Chunking our Dataset (7:31)
Creating our Custom Chunking Function (16:06)
Tokenizing our Dataset (9:30)
Running our Chunking Function (4:30)
Understanding the Entire Chunking Process (8:32)
Uploading the Training Data to AWS S3 (5:53)
Course Check-In

Understanding LoRA and Setting up HuggingFace Estimator

Setting Up Hyperparameters for the Training Job (6:47)
Creating our HuggingFace Estimator in Sagemaker (6:45)
Introduction to Low-rank adaptation (LoRA) (8:11)
LoRA Numerical Example (10:55)
LoRA Summarization and Cost Saving Calculation (9:08)
(Optional) Matrix Multiplication Refresher (4:45)
Understanding LoRA Programatically Part 1 (12:32)
Understanding LoRA Programatically Part 2 (5:48)
Unlimited Updates

Improving Training Speed with Bfloat 16

Bfloat16 vs Float32 (8:10)
Comparing Bfloat16 Vs Float32 Programatically (6:32)
Implement a New Life System - at end of 3rd section

Setting up the QLoRA Training Script with Mixed Precision & Double Quantization

Setting up Imports and Libraries for the Train Script (7:19)
Argument Parsing Function Part 1 (7:56)
Argument Parsing Function Part 2 (10:54)
Understanding Trainable Parameters Caveats (14:30)
Introduction to Quantization (7:35)
Identifying Trainable Layers for LoRA (7:19)
Setting up Parameter Efficient Fine Tuning (4:36)
Implement LoRA Configuration and Mixed Precision Training (10:34)
Understanding Double Quantization (4:21)
Creating the Training Function Part 1 (14:14)
Creating the Training Function Part 2 (7:16)
Exercise: Imposter Syndrome
Finishing our Sagemaker Script (5:09)
Gaining Access to Powerful GPUs with AWS Quotas (5:10)
Final Fixes Before Training (3:54)

Running our Fine Tuning Script for our LLM

Starting our Training Job (7:15)
Inspecting the Results of our Training Job and Monitoring with Cloudwatch (11:23)

Deploying our Fine Tuned LLM

Deploying our LLM to a Sagemaker Endpoint (17:57)
Testing our LLM in Sagemaker Locally (8:18)
Creating the Lambda Function to Invoke our Endpoint (8:55)
Creating API Gateway to Deploy the Model Through the Internet (2:36)
Implementing our Streamlit App (5:11)
Streamlit App Correction (3:26)

Cleaning up Resources

Congratulations and Cleaning up AWS Resources (2:38)

Where To Go From Here?

Thank You! (1:17)
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