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Customizing Generative AI: Fine-Tuning and Transfer Learning Techniques using AWS Bedrock
Customizing Generative AI: Fine-Tuning and Transfer Learning Techniques using AWS Bedrock
📑 Lessons : 7 Lesson
🕒 Duration : 3 days
🎚️ Skill level : Advanced
📑 Language : English
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This in-depth course explores the customization of generative AI models through fine-tuning and transfer learning techniques using AWS Bedrock. Designed for machine learning practitioners and data scientists, the course will provide a thorough understanding of generative models and how to leverage AWS Bedrock’s powerful tools to adapt pre-trained models for specific tasks. Participants will engage in hands-on projects to apply these techniques in various scenarios, enhancing their skills in building tailored AI solutions. By the end of the course, learners will be equipped to implement fine-tuning and transfer learning strategies effectively within their own projects.
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What you'll learn
✔️ Exploring AWS Bedrock for generative AI solutions
✔️ Fine-tuning pre-trained models for domain-specific tasks
✔️ Transfer learning techniques to adapt generative models
✔️ Customizing generative models for unique use cases
✔️ Deploying and managing AI models on AWS infrastructure
Prerequisites
✔️ Familiarity with generative AI models and transfer learning
✔️ Basic understanding of AWS services
Course Content
Module 1: Introduction to Generative AI and AWS Bedrock (Duration: 2 hours)
✔️ Overview of generative AI models and their applications
✔️ Introduction to AWS Bedrock and its capabilities
✔️ Hands-on: Setting up an AWS Bedrock environment
Module 2: Understanding Fine-Tuning and Transfer Learning (Duration: 3 hours)
✔️ Key concepts in fine-tuning and transfer learning
✔️ Benefits and challenges of these techniques
✔️ Hands-on: Exploring model architectures and selecting pre-trained models
Module 3: Fine-Tuning Generative Models (Duration: 5 hours)
✔️ Techniques for fine-tuning generative models on specific datasets
✔️ Hyperparameter tuning and optimization strategies
✔️ Hands-on: Fine-tuning a generative AI model using AWS Bedrock
Module 4: Transfer Learning Techniques in Practice (Duration: 5 hours)
✔️ Implementing transfer learning across different domains
✔️ Case studies showcasing successful applications
✔️ Hands-on: Applying transfer learning to a new task with AWS Bedrock
Module 5: Evaluating Model Performance (Duration: 3 hours)
✔️ Metrics and methodologies for assessing generative models
✔️ Techniques for robust evaluation and validation
✔️ Hands-on: Evaluating the performance of fine-tuned models
Module 6: Ethics and Best Practices in Generative AI (Duration: 3 hours)
✔️ Ethical considerations in deploying generative AI solutions
✔️ Best practices for responsible AI usage
✔️ Discussion: Future trends and considerations in generative AI
Module 7: Capstone Project: Customizing a Generative AI Model (Duration: 3 hours)
✔️ Defining a project objective for customization
✔️ Building and deploying a fine-tuned generative AI model using AWS Bedrock
✔️ Hands-on: Presenting the final project and outcomes
Frequently Asked Questions
📍 Question1: What is AWS Bedrock, and how does it relate to generative AI?
Answer: AWS Bedrock is a service provided by Amazon Web Services that allows developers to build and scale generative AI applications using pre-trained foundational models. It simplifies the integration of generative AI capabilities into applications without the need for extensive machine learning expertise.
📍 Question2: Who should enroll in this AWS generative AI course?
Answer: This course is designed for machine learning practitioners, data scientists, and AI professionals who have a basic understanding of AWS and wish to specialize in generative AI, particularly in fine-tuning and transfer learning techniques.
📍 Question3: What are the key benefits of completing this AWS training online?
Answer: By completing this training, participants will gain hands-on experience in customizing AI models using AWS Bedrock, enhancing their ability to deploy and manage AI applications efficiently. This certification will also boost career opportunities in AI and cloud computing.
📍 Question4: Do I need prior experience with AWS to take this course?
Answer: Yes, a basic understanding of AWS services is required to maximize the learning experience. Familiarity with cloud computing and AI models will help participants grasp the advanced concepts covered in the course.
📍 Question5: How is this course different from other AWS cloud computing courses?
Answer: Unlike general AWS cloud computing courses, this course focuses on the practical applications of generative AI using AWS Bedrock, emphasizing fine-tuning and transfer learning techniques to customize pre-trained models.
📍 Question6: Will this course prepare me for AWS certification?
Answer: Yes, the course provides the knowledge and skills necessary to advance your expertise in AI and cloud computing, which is beneficial for various AWS training and certification paths, particularly those focusing on AI and machine learning.
📍 Question7: How is the course delivered?
Answer: The course is delivered virtually through live sessions, allowing interactive learning and direct engagement with instructors. It includes practical projects to reinforce the theoretical knowledge gained.
📍 Question8: What is the format of the capstone project?
Answer: The capstone project involves developing a customized generative AI model using the techniques learned during the course. This project helps solidify the concepts and provides a tangible output that participants can showcase.
📍 Question9: Are there any discounts available for this course?
Answer: Yes, the course is currently offered at a discounted rate of $499.00, reduced from the original price of $2000.00. This makes it an excellent opportunity for learners to acquire advanced skills at a fraction of the cost.
📍 Question10: What kind of support can I expect during the course?
Answer: Participants will have access to expert instructors who can provide guidance and answer questions during the live sessions. Additionally, all course materials and resources are made available for review and further study.
