AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker

11h 59m 21s
English
Paid

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 TitleDurationAccess
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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