Amazon Bedrock Masterclass: The Ultimate Generative Ai Guide
Date: 6 November 2023 | Posted by: oaxino
Amazon Bedrock Masterclass: The Ultimate Generative Ai Guide
Published 11/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.07 GB | Duration: 3h 25m
From Basic to Advanced Generative Artificial Intelligence on AWS
Published 11/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.07 GB | Duration: 3h 25m
From Basic to Advanced Generative Artificial Intelligence on AWS
What you'll learn
Acquire a comprehensive understanding of Amazon Bedrock's architecture and its integration with AWS services to deploy generative AI applications.
Gain proficiency in utilizing the Amazon Bedrock API to access and interact with foundation models from various AI companies for generative tasks.
Master the techniques of prompt engineering to effectively communicate with language models and obtain desired responses for various AI applications.
Develop skills to customize, build, and scale generative AI applications using Amazon Bedrock, ensuring privacy and without managing infrastructure.
Requirements
Proficiency in Python programming is essential.
A foundational understanding of artificial intelligence and machine learning concepts will be beneficial to grasp the advanced topics covered in the course.
A computer with internet access to utilize AWS services and to participate in the online course.
An AWS account to practice and apply the concepts learned in the course.
Description
Embark on a transformative learning journey with the "Amazon Bedrock Masterclass: A Guide to Generative AI on AWS." This comprehensive course is meticulously designed to equip you with the knowledge, skills, and practical expertise necessary to excel in the rapidly evolving domain of Generative Artificial Intelligence (AI) using Amazon's robust Bedrock platform.The course commences with a robust introduction to Amazon Bedrock, providing a solid foundation to understand the platform's capabilities and offerings. As a fully managed service on AWS, Amazon Bedrock simplifies the development of generative AI applications by providing access to high-performing foundation models from leading AI companies. This initial module sets the stage for the subsequent exploration and deep-dive into the myriad features that Amazon Bedrock offers, thus enabling a nuanced understanding and effective utilization of the platform. As you progress through the course, you'll delve into the technical intricacies of working with the Amazon Bedrock API, a critical skill for leveraging the platform's capabilities to the fullest. Your learning journey will then advance to exploring the advanced features of Amazon Bedrock, thus preparing you to handle complex generative AI projects.The course then transitions to a practical approach, focusing on building generative AI applications with Bedrock. This module is designed to transition theoretical knowledge into practical expertise, enabling you to conceptualize, develop, and deploy generative AI applications effectively. Afterwards, the course underscores the importance of adhering to best practices while also providing a thorough understanding of the pricing model of Amazon Bedrock, thereby enabling informed and cost-effective decision-making.The culmination of this masterclass is the Capstone Project, where you'll apply the amassed knowledge and skills in a real-world project, showcasing your competency in utilizing Amazon Bedrock for generative AI applications. This hands-on project is an opportunity to integrate and apply the learning from each module in a practical scenario, thus solidifying your understanding and readiness to tackle real-world generative AI challenges using Amazon Bedrock.The "Amazon Bedrock Masterclass: A Guide to Generative AI on AWS" is more than just a course; it's a pathway to mastering generative AI on one of the most sophisticated platforms. The structured modules, practical insights, and the capstone project collectively ensure a rich, engaging, and rewarding learning experience, propelling you towards becoming a proficient practitioner of generative AI on AWS.
Overview
Section 1: Introduction to Amazon Bedrock
Lecture 1 Introduction to the course
Lecture 2 Setting up your AWS account
Lecture 3 What is Amazon Bedrock?
Lecture 4 Parameters and Considerations
Lecture 5 Crafting Your Initial Prompt
Section 2: Exploring Bedrock Features
Lecture 6 Introduction to Using the Amazon Bedrock Playground
Lecture 7 Using the Chat and Text Playground
Lecture 8 Using the Image Generation Playground
Lecture 9 A Note for Getting through Neutrality
Lecture 10 Using Examples from Amazon Bedrock
Lecture 11 Fine Tuning Foundational Models
Lecture 12 Further References for Using Amazon Bedrock
Section 3: Working with the Amazon Bedrock API
Lecture 13 Foundational Models on Amazon Bedrock
Lecture 14 Understanding Why we Use the API
Lecture 15 Installing Python and Jupyter Notebook
Lecture 16 Performing Text Generation using the Bedrock API
Lecture 17 An Introduction to using Stability AI
Lecture 18 Using Stability AI Through the Amazon Bedrock API
Lecture 19 Generating Code on the Amazon Bedrock API
Section 4: Advanced Features with Amazon Bedrock
Lecture 20 Introduction to Creating Context-driven Chatbots
Lecture 21 Coding Up a Chatbot with Context on Amazon Bedrock
Lecture 22 Introduction to Retrieval Augmented Generation
Lecture 23 Using Retrieval Augmented Generation with Amazon Bedrock API
Section 5: Building Generative AI Applications with Bedrock
Lecture 24 Introduction to Building Generative AI Applications with Amazon Bedrock
Lecture 25 An example of Building a Frontend of an AI Application
Lecture 26 An example of Building a Backend of an AI Application
Lecture 27 Setting Up the Lambda and API Gateway
Lecture 28 A Demo of an AI Application
Section 6: Best Practices and Pricing
Lecture 29 Best Practices
Lecture 30 Pricing
Lecture 31 Challenges
Lecture 32 Where to Go from Here
Section 7: Capstone Project and Final Exam
Lecture 33 Wrap-Up
Developers and AI Practitioners,Data Scientists and Machine Learning Engineers,AI Enthusiasts and Hobbyists,Technical Managers and Decision-makers,Educators and AI Researchers
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