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

Name: AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker
Price: 9 USD
Availability: InStock

11h 59m 21s

English

Paid

August 30, 2024

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
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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