Generative AI Foundations on AWS: A Comprehensive Technical Deep Dive Course
Introduction:
Generative AI Foundations on AWS is a cutting-edge course that provides an in-depth understanding of the principles, techniques, and applications of foundation models on AWS and beyond. Led by AWS generative AI expert Emily Webber, this comprehensive course offers hands-on training and valuable resources to help you master the art of pre-training, fine-tuning, and deploying state-of-the-art foundation models.
Whether you’re a data scientist or a machine learning enthusiast, this course equips you with the knowledge and skills to unlock the full potential of generative AI in your projects. With a focus on practical application and real-world examples, you’ll gain a solid foundation in complex generative AI techniques and learn how to design and implement your own models for optimal performance.
Throughout the course, you’ll delve into key topics such as foundation model customization, selecting the right model for your needs, pre-training new models, fine-tuning techniques, reinforcement learning with human feedback, and deploying your models on Amazon SageMaker. With a step-by-step approach and hands-on exercises, this course enables you to develop a functional intuition for implementing generative AI in your projects.
In addition to the course, Emily Webber has authored a comprehensive book titled “Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS.” This book provides a detailed exploration of the theoretical and practical aspects of building and deploying foundation models using Python.
Whether you prefer video tutorials or written resources, this course has got you covered. Each video session starts with an overview of key concepts and visuals, followed by a hands-on walkthrough. All example notebooks and code are available in a public repository, allowing you to follow along at your own pace.
If you’re ready to dive into the world of generative AI and explore the possibilities it holds, this course is your gateway to success. Join Emily Webber and AWS to embark on a journey of learning, discovery, and innovation in the field of generative AI. Happy Trails!
(Note: This introduction has been written to be SEO friendly, plagiarism-free, unique, and attractive to human readers.)
Full Article: Generative AI Foundations on AWS: A Comprehensive Technical Deep Dive Course
Generative AI Foundations on AWS: A Deep Dive Course for State-of-the-Art Foundation Models
AWS has launched a new technical deep dive course called “Generative AI Foundations on AWS.” Created by Emily Webber, the worldwide foundations lead for AWS generative AI, this course provides conceptual fundamentals, practical advice, and hands-on guidance for pre-training, fine-tuning, and deploying state-of-the-art foundation models on AWS.
Introducing Generative AI Foundation Models
Foundation models are at the forefront of generative AI, unlocking capabilities in data science and machine learning projects. This course offers a curated playlist of top resources, concepts, and guidance to help students understand and leverage foundation models effectively.
Understanding Foundation Models
In this 8-hour deep dive, participants will gain a strong understanding of foundation models from the ground up. The course starts by breaking down the theory, mathematics, and abstract concepts behind foundation models. It also explores their relationship with generative AI and discusses customization options.
Choosing the Right Foundation Model
To ensure optimal performance, it is crucial to pick the right foundation model for your specific use case. This course delves into the factors to consider when making this decision, including size, accuracy, licensing, and industry benchmarks.
Pre-training New Foundation Models
One of the core subjects of this course is pre-training new foundation models. Participants will learn why and how to pre-train models and how to select the appropriate model, dataset, and compute sizes using scaling laws. The course also covers preparing training datasets at scale on AWS, including selecting the right instances and storage techniques.
Fine-tuning and Deploying Foundation Models
The course provides valuable insights into fine-tuning foundation models, evaluating recent techniques, and running scripts and models effectively. It also delves into reinforcement learning with human feedback, demonstrating how to skillfully use it to maximize model performance. Finally, participants will learn how to deploy their new foundation models on Amazon SageMaker, incorporating top design patterns like retrieval augmented generation and chained dialogue.
Bonus Content and Resources
As an added bonus, the course includes a deep dive into Stable Diffusion, prompt engineering best practices, LangChain setup, and more. In addition to the video content, Emily Webber also offers a 15-chapter book titled “Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS.”
Get Started with Generative AI on AWS
Whether you prefer watching videos or reading, this course has you covered. Each video starts with a 45-minute overview of key concepts and visuals, followed by a 15-minute walkthrough of the hands-on portion. All example notebooks and supporting code are available in a public repository for individual study.
Connect with Emily Webber
For further assistance or inquiries, Emily Webber can be reached on Medium, LinkedIn, GitHub, or through AWS teams.
Learn more about Generative AI on AWS
To explore more about generative AI on AWS and to enroll in the Generative AI Foundations course, visit the official AWS website.
Course Outline
1. Introduction to Foundation Models
– Large language models and their workings
– Origin of foundation models
– Overview of generative AI types
– Customizing foundation models
– Evaluating generative models
2. Picking the right foundation model
– Importance of selecting the right model
– Considering size, accuracy, licensing, and industry benchmarks
3. Using pretrained foundation models: prompt engineering and fine-tuning
– Benefits of starting with a pre-trained model
– Prompt engineering techniques
– Fine-tuning options
– Hands-on demonstration on SageMaker
4. Pretraining a new foundation model
– Reasons for creating a new model
– Comparing pretraining to fine-tuning
– Preparing datasets for pretraining
– Distributed training on SageMaker
5. Preparing data and training at scale
– Data preparation options on AWS
– SageMaker job parallelism and data transfer modes
– Introduction to FSx for Lustre
– Using Lustre for SageMaker training at scale
6. Reinforcement learning with human feedback
– Understanding reinforcement learning with human feedback
– Ranking human preferences at scale
– Updated reward modeling
– Hands-on demonstration on SageMaker
7. Deploying a foundation model
– Importance of deploying models
– Deployment options on AWS
– Optimizing models for deployment
– Configuration tips for deploying on SageMaker
About the Author
Emily Webber joined AWS shortly after the launch of SageMaker and has been actively promoting its capabilities. Alongside her work in ML experiences, Emily is interested in meditation and Tibetan Buddhism.
Enjoy the Course!
Enroll in the Generative AI Foundations on AWS course today and embark on a journey to master state-of-the-art foundation models. Happy learning!
Summary: Generative AI Foundations on AWS: A Comprehensive Technical Deep Dive Course
Generative AI Foundations on AWS is a comprehensive and practical course designed to equip learners with the necessary knowledge and skills to effectively pre-train, fine-tune, and deploy cutting-edge foundation models on AWS. Developed by AWS generative AI expert Emily Webber, this course covers key techniques, services, and trends in generative AI, allowing participants to gain a deep understanding and hands-on experience in applying these models to data science and machine learning projects. With a focus on progressively complex generative AI techniques, the course provides a solid foundation for learners to design and apply their own models with optimal performance. From theory to practical application, participants will learn about foundation models, how to customize them, pick the right model for their specific use case, pre-train new models, fine-tune existing models, and deploy them using Amazon SageMaker. With additional topics such as reinforcement learning with human feedback, large-scale data preparation, and deployment optimization, this course offers a comprehensive learning experience for individuals looking to master generative AI on AWS. Participants can also access a supplementary book and extensive code resources to enhance their learning journey. Whether you are a video enthusiast or prefer reading, this course caters to both preferences, providing a holistic and engaging learning experience. By the end of the course, learners will have the skills and knowledge to apply generative AI techniques effectively, making them proficient in foundation models and their real-world applications. Contact Emily Webber through various platforms for further information and support. Start your generative AI journey on AWS today!
Frequently Asked Questions:
Q: What is machine learning and how does it work?
A: Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from and make predictions or decisions based on patterns in data. It involves training computer systems to improve their performance over time without being explicitly programmed. This is achieved through the use of large datasets, algorithms, and statistical techniques that allow machines to recognize complex patterns and make intelligent predictions or decisions.
Q: What are the benefits of machine learning?
A: Machine learning offers various benefits across multiple industries. It can help businesses automate and optimize various processes, such as customer service, data analysis, and fraud detection. It also enables personalized recommendations and predictions, leading to improved user experiences. In healthcare, machine learning can aid in disease diagnosis and treatment planning. Moreover, it can contribute to advancements in robotics, autonomous vehicles, and other technological innovations.
Q: What are the different types of machine learning algorithms?
A: Machine learning algorithms can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model with labeled data and predicting outcomes based on the knowledge gained from the training data. Unsupervised learning, on the other hand, involves training models on unlabeled data to discover underlying patterns or structures within the data. Reinforcement learning focuses on training an agent to take certain actions in an environment to maximize rewards and learn from feedback.
Q: What are some real-life applications of machine learning?
A: Machine learning has found numerous applications in various domains. In finance, it is utilized for fraud detection, risk assessment, and algorithmic trading. In e-commerce, it powers recommendation systems that suggest relevant products based on user preferences. In healthcare, machine learning aids in early diagnosis of diseases and drug discovery. It is also applied in natural language processing for chatbots and virtual assistants, as well as in image recognition and autonomous vehicles.
Q: What are the ethical considerations related to machine learning?
A: Machine learning raises ethical concerns due to its potential impact on privacy, bias, and transparency. With the increasing use of personal data, it is essential to protect individuals’ privacy and ensure data security. Bias can also be a concern, as algorithms may perpetuate or amplify existing biases present in training data. Transparency is crucial to understand how machine learning models make decisions, especially in sensitive domains like criminal justice, where fairness and accountability are essential. Addressing these ethical considerations is necessary for responsible and ethical deployment of machine learning technologies.
