AWS SageMaker Crash Course – Train & Deploy AI Models
Introduction
Imagine training, building, and deploying AI models that power awesome applications—without needing years of experience or expensive equipment. Sounds impossible? Well, not anymore!
This crash course is going to introduce you to AWS SageMaker, Amazon's go-to platform for building, training, and deploying AI models—an essential skill for AI engineers.
Guiding you through this journey is Ibikunle Samuel, a senior machine learning engineer who has built AI solutions for many companies. In this Intro to AWS SageMaker course, he’ll teach you:
✅ How to create your AWS environment
✅ The magic of integrating Hugging Face models with SageMaker
✅ Tokenization and encoding for NLP
✅ And so much more!
If you love this AWS SageMaker introduction from Samuel, I highly recommend checking out his Complete AWS SageMaker Bootcamp Course, where he covers:
- The mathematics behind Transformers
- Deploying production-grade solutions
- Load testing your deployed models
Alright, enough from me! If you’re ready to ride the AI wave and take your first step toward becoming an AI Engineer, Data Scientist, or Machine Learning Specialist, then this course is your perfect starting point.
Oh, and one last thing—if you enjoy this crash course, please help us by liking this article and leaving a comment with your thoughts or questions below.
What do these fields have in common? Whether it’s computer vision, finance, or healthcare, they all use machine learning and artificial intelligence.
I’ve gathered years of experience and structured this course in an easy-to-follow way. By the end of this course, you’ll be among the top 10% of AI engineers—and this knowledge will help you land your dream job.
Course Overview
In this course, we will do everything the production-ready way using AWS SageMaker—Amazon’s flagship platform for building, training, and deploying machine learning models.
This means:
❌ No Google Colab for training or storing models
❌ No local storage (Google Drive, local files)
❌ No manual dependencies (Conda/Anaconda setup)
✅ Everything will be done professionally in the AWS Cloud.
Why AWS?
- It’s the most prominent cloud provider
- It dominates the cloud computing market
- It’s an industry standard
1️⃣ Setting Up Your AWS SageMaker Environment
We'll start by setting up your AWS environment with:
✔️ PyTorch and TensorFlow on AWS
✔️ Security Best Practices (IAM Roles)
✔️ SageMaker Domains & Environments
💡 Don’t worry if you don’t know these terms—I’ll explain everything from scratch!
Understanding Pricing
Many people fear cloud computing costs, so they stick with Google Colab. In this course, I’ll teach you:
🔹 How to check pricing for every AWS service
🔹 How to avoid unnecessary costs
🔹 How to use AWS Free Tier options
🚀 Before using any paid AWS service, I’ll let you know in advance!
2️⃣ Introduction to Hugging Face on AWS
After setting up AWS, we’ll explore Hugging Face models and how they integrate with SageMaker.
Topics include:
🔹 Overview of Hugging Face models in AWS
🔹 Deploying a sentiment analysis model on SageMaker
🔹 Understanding Auto-Scaling for endpoints (crucial for production)
3️⃣ Capstone Project: Multi-Class Text Classification
Our main hands-on project will be building a News Headline Categorizer that classifies news headlines into categories like:
📌 Entertainment
📌 Technology
📌 Science
📌 Health
📌 And more!
Project Workflow:
✔️ Exploratory Data Analysis (EDA) & Visualization (AWS SageMaker Notebooks)
✔️ Data Storage & Retrieval using AWS
✔️ Fine-tuning a Large Language Model (DistilBERT)
4️⃣ Fine-Tuning DistilBERT on AWS SageMaker
You’ll learn how to:
🔹 Modify model architectures with PyTorch
🔹 Add Dropout, Linear Layers, Softmax Layers
🔹 Train models using custom datasets
💡 Multi-class text classification is one of the hottest AI applications today!
5️⃣ Choosing the Right GPU & Training Optimization
I’ll introduce:
✔️ How to select the best GPU for your model
✔️ Understanding AWS pricing for GPUs
✔️ Monitoring training jobs using AWS CloudWatch
6️⃣ Deploying AI Models in Production
This is where most courses stop—but we’ll go further by learning how to deploy models professionally.
🔹 Deploying as Real-Time Inference API
🔹 Connecting with React, Angular, Vue front-ends
🔹 Testing with Postman
✔️ Choosing the right CPU server for deployment
✔️ Understanding server pricing
7️⃣ Load Testing & Performance Optimization
How many requests can your AI model handle before failing? In this section, we’ll:
🔹 Simulate thousands of requests
🔹 Analyze model latency, CPU & GPU utilization
🔹 Optimize server selection based on real-world demand
8️⃣ Secure API Deployment with Amazon API Gateway & Lambda
We’ll securely deploy our AI model over the internet using:
✔️ Amazon API Gateway
✔️ AWS Lambda Functions
💡 No worries if you’re new to these concepts—I’ll explain them from scratch!
Optional: Mathematics Behind Large Language Models (Highly Recommended!)
In this non-coding section, we’ll cover:
✔️ Tokenization & Word Embeddings
✔️ Positional Encoding (Sine & Cosine Functions)
✔️ Query, Key, and Value Matrices for Attention Mechanisms
✔️ Multi-Head Attention & Transformer Architectures
Understanding these mathematical foundations will make it easier to debug and fine-tune AI models.
Final Thoughts & Next Steps
By the end of this course, you will:
✅ Be proficient in AWS SageMaker
✅ Know how to train and deploy real-world AI models
✅ Understand production-ready cloud ML workflows
If you enjoyed this crash course, be sure to check out the Complete AWS SageMaker Bootcamp for advanced topics and career guidance!
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