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.
AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker
11h 59m 21s
English
Paid
Additional
About the Author: Zero To Mastery
Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.
The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.
The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.
Watch Online 84 lessons
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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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Frequently asked questions
What are the prerequisites for enrolling in this course?
Before enrolling in this course, you should have a basic understanding of Python programming and machine learning concepts. Familiarity with deep learning frameworks like PyTorch is also beneficial, as the course includes lessons on setting up SageMaker Server with PyTorch and creating custom model architectures using PyTorch.
What projects will I work on during the course?
Throughout the course, you'll engage in practical exercises such as building a sentiment analysis application using HuggingFace models and setting up a multiclass text classification dataset. You'll also conduct exploratory data analysis and create custom model architectures with PyTorch, culminating in deploying and scaling an AI application with AWS SageMaker.
Who is the target audience for this course?
This course is designed for aspiring AI engineers and data scientists who wish to gain hands-on experience with AWS SageMaker. It is suitable for individuals looking to understand the full cycle of AI application development, from data preparation to model deployment and scaling.
How does this course compare in depth to other AI courses?
The course offers a detailed exploration of AI engineering using AWS SageMaker, covering 84 lessons from account setup to advanced topics like multi-head attention and custom model architecture creation. Unlike many introductory courses, it delves into practical application and deployment in a cloud environment, providing a comprehensive view of AI project lifecycle management.
What specific tools and platforms are covered in this course?
The course centers around AWS SageMaker, with lessons dedicated to setting up and utilizing SageMaker Studio. You'll learn to work with PyTorch for model creation and HuggingFace for sentiment analysis. Additionally, it covers AWS S3 for data storage and IAM roles for security practices.
What topics are not covered in this course?
The course does not cover non-AWS cloud platforms or introductory Python programming. It focuses specifically on AWS SageMaker and related AWS services, so experience with other cloud providers like Google Cloud or Microsoft Azure is not included.
What is the time commitment required to complete the course?
The course comprises 84 lessons. While the exact runtime is not specified, given the depth of topics like tokenization, attention mechanisms, and deployment strategies, expect to invest several weeks to fully grasp and practice the material, assuming a part-time study schedule.