In this 4-hour crash course, you’ll learn the fundamentals of AWS SageMaker, Amazon’s go-to platform for building, training, and deploying AI models.
This crash course is led by Patrik Szepesi, a Senior Machine Learning Engineer who’s built AI solutions for Fortune 500 companies. Patrik will guide you through setting up your AWS environment, working with HuggingFace models, tokenization for NLP, and much more.
This is just the start of Patrik’s 12-hour AWS SageMaker Bootcamp, one of the most in-depth courses for mastering SageMaker. In the full course, you’ll dive into advanced topics and build real-world AI solutions to kickstart your career as an AI Engineer in 2025!
🤖 AI Engineering Bootcamp: https://zerotomastery.io/courses/ai-engineer-bootcamp-aws-sagemaker/
🤑 Use code: YTSAGE10 to get 10% OFF (for life!)
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⌨️ Course Github: https://github.com/patrikszepesi/LLM_course
🛣️ ML/AI Engineering Roadmap: https://zerotomastery.io/career-paths/become-a-machine-learning-engineer/
👍 Subscribe for more free tutorials and exclusive content: https://links.zerotomastery.io/youtube
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⏰ Timestamps:
00:00 Introduction
01:26 Course Breakdown
10:18 Setting Up Our AWS Account
15:00 Set Up IAM Roles + Best Practices
22:49 AWS Security Best Practices
30:01 Set Up AWS SageMaker Domain
32:33 UI Domain Change
33:25 Sagemaker Domain Creation Update Part 1
36:16 Sagemaker Domain Creation Update Part 2
39:32 Sagemaker Notebooks Update
51:41 Setting Up SageMaker Environment
57:00 SageMaker Studio and Pricing
1:05:54 Quota Increase
1:13:39 Setup: SageMaker Server + PyTorch
1:19:57 HuggingFace Models, Sentiment Analysis, and AutoScaling
1:38:42 Get Dataset for Multiclass Text Classification
1:44:55 Uploading Our Training Data to S3
1:48:58 Set Up IAM Roles + Best Practices
1:50:34 Exploratory Data Analysis - Part 1
2:04:05 Data Visualization and Best Practices
2:10:23 Set Up IAM Roles + Best Practices
2:21:41 Setting Up Our Training Job Notebook + Reasons to Use SageMaker
2:40:14 Python Script for HuggingFace Estimator
2:54:01 Creating Our Optional Experiment Notebook Part 1
2:57:32 Creating Our Optional Experiment Notebook Part 2
3:01:43 Encoding Categorical Labels to Numeric Values
3:15:17 Understanding the Tokenization Vocabulary
3:30:33 Encoding Tokens
3:41:39 Practical Example of Tokenization and Encoding
3:54:38 Final Takeaway

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💥 Who is this AI Engineering Bootcamp Course for?
https://zerotomastery.io/courses/ai-engineer-bootcamp-aws-sagemaker/
◾ Anyone who wants a step-by-step guide to learning to use AWS SageMaker, an end-to-end machine learning and AI tool, and be able to get hired as an AI Engineer
◾ Anyone who wants to launch or accelerate their career in AI
◾ Students, Developers, Machine Learning Engineers, Data Scientists, and AI Engineers who want to demonstrate practical, professional-level machine learning skills by actually building, training, and deploying real models to the cloud
◾ Anyone looking to expand their knowledge and toolkit when it comes to AI, Machine Learning and Deep Learning
◾ Bootcamp or online Amazon SageMaker tutorial graduates that want to go beyond the basics
◾ Students who are frustrated with their current progress with all of the beginner AWS SageMaker tutorials out there that don’t go beyond the basics and don’t give you real-world practice or skills you need to actually get hired
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Graduates of Zero To Mastery are now working at Google, Tesla, NVIDIA, Amazon, Apple, IBM, JP Morgan, Meta, NASA, Shopify + other top companies.
Many are also working as top-rated Freelancers getting paid $1,000s while working remotely around the world.
🎓 Here are just a few of them: https://zerotomastery.io/testimonials
This can be you.
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Become a Top 10% AI Engineer 👉 https://zerotomastery.io/courses/ai-engineer-bootcamp-aws-sagemaker/
#zerotomastery #sagemaker