English | Size: 1.9 GB
Genre: eLearning
Launch your career in AI with this course that teaches you to build, train, and deploy your own AI models using two of the most important AI tools used in the real world: AWS SageMaker and Hugging Face. No machine learning knowledge required!
What you’ll learn
Build and deploy cutting-edge artificial intelligence & machine learning models to the cloud
Utilize powerful pre-trained models from Hugging Face with AWS SageMaker
Uncover the mathematical secrets behind how Large Language Models work with a deep-dive into the Transformer architecture, tokenization, and more
Customize models to meet the needs of your AI applications using PyTorch to create unique solutions
Train and test models, ensuring they deliver accurate results every time
Learn best practices for monitoring and optimizing your models, including load testing and scaling for massive user demand
Why This Course?
Well, because it’s the best, most up-to-date, and practical AI Engineering Bootcamp course online that teaches you real-world AWS SageMaker skills and that lets you get hands-on so that you can actually use your skills in the real-world.
But of course we’re biased. So here’s a section-by-section breakdown of this AWS SageMaker course so that you can make up your own mind:
1. Introduction: Get an overview of what you’ll be doing in this course, meet the other students taking this course, and meet your instructor extraordinaire: Senior Machine Learning Engineer, Patrik Szepesi!
2. Setting Up Our AWS Environment and Best Practices: We’ll dive right away into establishing a solid foundation by setting up an AWS account, configuring IAM roles for security, and applying best practices. You’ll also learn to set up the AWS SageMaker domain, adjust UI settings, and understand the pricing structures within SageMaker Studio.
3. A Gentle Introduction to HuggingFace in Amazon SageMaker: You’ll then get a practical introduction to integrating HuggingFace with AWS SageMaker. The section covers setting up SageMaker with PyTorch and deploying pre-built HuggingFace models for tasks like sentiment analysis, including considerations for autoscaling.
4. Gathering a Dataset for Our Multiclass Text Classification Project: Time to get your hands dirty and learn how to source and prepare datasets for a multiclass text classification task. You’ll learn about acquiring datasets, creating S3 buckets, and uploading data effectively to AWS.
5. Exploratory Data Analysis: In this section you’ll engage in exploratory data analysis to uncover insights from your datasets, including learning about data visualization techniques and best practices to better understand underlying patterns that could influence subsequent modelling decisions.
6. Setting Up Our Training Notebook: This practical section helps you set up your training environments in Amazon SageMaker, including configuring notebooks and scripting with Python for HuggingFace estimators. This setup is crucial for efficient model training and iteration!
7. Introduction to Tokenizations and Encodings: This is such a cool section. You’ll explore the essential NLP (Natural Language Processing) processes of tokenization and encoding, which are fundamental to AI models like LLMs (Large Language Models). The lessons include hands-on activities for creating notebooks, understanding tokenization vocabulary, and practical examples of applying these techniques in model training.
8. Setting up Data Loading with PyTorch: Here you’ll learn to create dataset loader classes and configure PyTorch DataLoader, which are key components for handling large volumes of data efficiently during model training.
9. Choose Your Path: Time for another brief interlude! This will prepare you for the advanced topics ahead, helping you decide the path you’ll take in learning about the deeper technical details of machine learning and AI models. You can decide if you want the deep dive into mathematics or not (we know some of you don’t love the math, and that’s okay!).
10. Mathematics Behind Large Language Models and Transformers: This extensive section delves into the mathematical foundations and workings of Transformers and Large Language Models, including multi-head attention, positional encodings, and the theory behind attention mechanisms.
11. Customizing our Model Architecture in PyTorch: Learn to customize a DistilBERT model by adding dropout, linear layers, and ReLU activations. This customization is key for tailoring the model to specific classification tasks and datasets.
12. Creating the Accuracy, Training, and Validation Function: This section focuses on creating accurate, robust training and validation functions to ensure models are evaluated properly and perform well under various conditions.
13. Optimizer Functions, Model Parameters, Cross Entropy Loss Function: Understand and implement optimization functions, adjust model parameters, and delve into the mathematics of the cross entropy loss function, which is essential for training effective machine learning models.
14. Possible Resource Limit Errors Before Training and Deployment: Preparing for potential issues, this section teaches you how to handle resource limit errors by managing and requesting AWS quota increases.
15. Starting Our Training Job and Monitoring it in AWS CloudWatch: This is getting exciting! Because here you’ll start your training jobs in SageMaker and learn to monitor and debug these jobs using AWS CloudWatch, gaining insights into the health and performance of your models.
16. Deploying our Multiclass Text Classification Endpoint in SageMaker: This critical section covers the deployment of the trained multiclass text classification model as an Amazon SageMaker endpoint, ensuring you understand the steps to make your models available for real-world applications.
17. Load Testing Our Machine Learning Model: Just because a model is live, doesn’t mean it’s going to work! You need to perform load testing on your deployed models to evaluate performance and scalability, preparing them for robust and efficient real-world deployments.
18. Production Grade Deployment of Our Machine Learning Model: This section takes you through the steps of deploying your model at a production scale, including setting up AWS Lambda functions and API Gateway, and testing the deployment with tools like Postman. This is where the rubber really meets the road!
19. Cleaning Up Resources: Last but not least, we need to clean things up to make sure you’re using the real-world best practices! This section emphasizes the importance of cleaning up AWS resources to manage costs and maintain an efficient cloud environment.
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