AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker
11h 59m 21s
English
Paid
Course description
Learn to create full-cycle AI applications using AWS SageMaker: from collecting and preparing your own data to training and modifying models, as well as deploying and scaling your AI application in the real world.
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/ #1: AI Engineering Bootcamp: Learn AWS SageMaker with Patrik Szepesi
All Course Lessons (84)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | AI Engineering Bootcamp: Learn AWS SageMaker with Patrik Szepesi Demo | 01:36 | |
| 2 | Course Introduction | 08:43 | |
| 3 | Setting Up Our AWS Account | 04:32 | |
| 4 | Set Up IAM Roles + Best Practices | 07:40 | |
| 5 | AWS Security Best Practices | 07:02 | |
| 6 | Set Up AWS SageMaker Domain | 02:23 | |
| 7 | UI Domain Change | 00:43 | |
| 8 | Setting Up SageMaker Environment | 05:09 | |
| 9 | SageMaker Studio and Pricing | 08:45 | |
| 10 | Setup: SageMaker Server + PyTorch | 06:09 | |
| 11 | HuggingFace Models, Sentiment Analysis, and AutoScaling | 18:35 | |
| 12 | Get Dataset for Multiclass Text Classification | 06:04 | |
| 13 | Creating Our AWS S3 Bucket | 03:53 | |
| 14 | Uploading Our Training Data to S3 | 01:27 | |
| 15 | Exploratory Data Analysis - Part 1 | 13:22 | |
| 16 | Exploratory Data Analysis - Part 2 | 06:08 | |
| 17 | Data Visualization and Best Practices | 11:09 | |
| 18 | Setting Up Our Training Job Notebook + Reasons to Use SageMaker | 18:25 | |
| 19 | Python Script for HuggingFace Estimator | 13:37 | |
| 20 | Creating Our Optional Experiment Notebook - Part 1 | 03:22 | |
| 21 | Creating Our Optional Experiment Notebook - Part 2 | 04:02 | |
| 22 | Encoding Categorical Labels to Numeric Values | 13:25 | |
| 23 | Understanding the Tokenization Vocabulary | 15:06 | |
| 24 | Encoding Tokens | 10:57 | |
| 25 | Practical Example of Tokenization and Encoding | 12:49 | |
| 26 | Creating Our Dataset Loader Class | 16:57 | |
| 27 | Setting Pytorch DataLoader | 15:10 | |
| 28 | Which Path Will You Take? | 01:32 | |
| 29 | DistilBert vs. Bert Differences | 04:47 | |
| 30 | Embeddings In A Continuous Vector Space | 07:41 | |
| 31 | Introduction To Positional Encodings | 05:14 | |
| 32 | Positional Encodings - Part 1 | 04:15 | |
| 33 | Positional Encodings - Part 2 (Even and Odd Indices) | 10:11 | |
| 34 | Why Use Sine and Cosine Functions | 05:09 | |
| 35 | Understanding the Nature of Sine and Cosine Functions | 09:53 | |
| 36 | Visualizing Positional Encodings in Sine and Cosine Graphs | 09:25 | |
| 37 | Solving the Equations to Get the Values for Positional Encodings | 18:08 | |
| 38 | Introduction to Attention Mechanism | 03:03 | |
| 39 | Query, Key and Value Matrix | 18:11 | |
| 40 | Getting Started with Our Step by Step Attention Calculation | 06:54 | |
| 41 | Calculating Key Vectors | 20:06 | |
| 42 | Query Matrix Introduction | 10:21 | |
| 43 | Calculating Raw Attention Scores | 21:25 | |
| 44 | Understanding the Mathematics Behind Dot Products and Vector Alignment | 13:33 | |
| 45 | Visualizing Raw Attention Scores in 2D | 05:43 | |
| 46 | Converting Raw Attention Scores to Probability Distributions with Softmax | 09:17 | |
| 47 | Normalization | 03:20 | |
| 48 | Understanding the Value Matrix and Value Vector | 09:08 | |
| 49 | Calculating the Final Context Aware Rich Representation for the Word "River" | 10:46 | |
| 50 | Understanding the Output | 01:59 | |
| 51 | Understanding Multi Head Attention | 11:56 | |
| 52 | Multi Head Attention Example and Subsequent Layers | 09:52 | |
| 53 | Masked Language Learning | 02:30 | |
| 54 | Exercise: Imposter Syndrome | 02:57 | |
| 55 | Creating Our Custom Model Architecture with PyTorch | 17:15 | |
| 56 | Adding the Dropout, Linear Layer, and ReLU to Our Model | 15:32 | |
| 57 | Creating Our Accuracy Function | 13:05 | |
| 58 | Creating Our Train Function | 19:09 | |
| 59 | Finishing Our Train Function | 08:18 | |
| 60 | Setting Up the Validation Function | 13:41 | |
| 61 | Passing Parameters In SageMaker | 04:06 | |
| 62 | Setting Up Model Parameters For Training | 04:28 | |
| 63 | Understanding The Mathematics Behind Cross Entropy Loss | 05:40 | |
| 64 | Finishing Our Script.py File | 06:57 | |
| 65 | Quota Increase | 07:36 | |
| 66 | Starting Our Training Job | 08:16 | |
| 67 | Debugging Our Training Job With AWS CloudWatch | 14:17 | |
| 68 | Analyzing Our Training Job Results | 05:47 | |
| 69 | Creating Our Inference Script For Our PyTorch Model | 08:35 | |
| 70 | Finishing Our PyTorch Inference Script | 09:13 | |
| 71 | Setting Up Our Deployment | 07:31 | |
| 72 | Deploying Our Model To A SageMaker Endpoint | 08:55 | |
| 73 | Introduction to Endpoint Load Testing | 04:20 | |
| 74 | Creating Our Test Data for Load Testing | 10:03 | |
| 75 | Upload Testing Data to S3 | 01:04 | |
| 76 | Creating Our Model for Load Testing | 03:59 | |
| 77 | Starting Our Load Test Job | 07:15 | |
| 78 | Analyze Load Test Results | 10:17 | |
| 79 | Deploying Our Endpoint | 03:51 | |
| 80 | Creating Lambda Function to Call Our Endpoint | 10:27 | |
| 81 | Setting Up Our AWS API Gateway | 05:28 | |
| 82 | Testing Our Model with Postman, API Gateway and Lambda | 05:40 | |
| 83 | Cleaning Up Resources | 02:52 | |
| 84 | Thank You! | 01:18 |
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