Recently amazon Launched an e-commerce company that launched Free AI courses And Got a free Certificate In the ever-evolving realm of artificial intelligence (AI), language models (LMs) have emerged as powerful tools capable of generating text, translating languages, and answering questions in an informative way. While building LMs traditionally required extensive technical expertise,it has introduced a free online course, “Building Language Models on AWS,” designed to democratize LM development and empower individuals to create their own AI language companions.
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About the Amazon
It was founded by Jeff Bezos from his garage in Bellevue, Washington, on July 5, 1994. Initially an online marketplace for books, it has expanded into a multitude of product categories, a strategy that has earned it the moniker The Everything Store. It has multiple subsidiaries including Web Services (cloud computing), Zoox (autonomous vehicles), Kuiper Systems (satellite Internet), and Lab126 (computer hardware R&D). Its other subsidiaries include Ring, Twitch, IMDb, and Whole Foods Market. Its acquisition of Whole Foods in August 2017 for US$13.4 billion substantially increased its footprint as a physical retailer.
About the Free AI Courses Program
This course is designed for data scientists and machine learning developers who are interested in building generative artificial intelligence (generative AI) applications using either the Amazon Bedrock API or LangChain integration. In this course, you will learn about the architecture patterns to build applications for key generative AI use cases.
The modules in this course prepare you to work through examples of generating and summarizing text, question answering, and a chatbot. The labs demonstrate the use of Amazon Bedrock models by using API calls, SDKs, and open source tools, such as LangChain.
•Course level: Advanced
•Duration: 4 hours
Activities
This course includes eLearning interactions, knowledge checks, and labs.
Course objectives
In this course, you will learn to:
•Identify the components of a generative AI application and how to customize a foundation model (FM)
•Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
•Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
•Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
•Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications
•Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach
Intended audience
This course is intended for:
•Data scientists
•Machine learning (ML) developers
Prerequisites
We recommend that attendees of this course have:
•Intermediate to expert-level proficiency with Python programming language
•AWS Technical Essentials
•Practical Data Science with Amazon SageMaker (intermediate)
•Amazon Bedrock Getting Started (Fundamental)
•Foundations of Prompt Engineering (Intermediate)
Course outline
Module 1: Introduction to Amazon Bedrock
•Building Generative AI Applications on Amazon Bedrock
•Applications and Use Cases
•Topics Covered in Future Modules
•Conclusion
Module 2: Application Components
•Overview of Generative AI Application Components
•Foundation Models and the FM Interface
•Working with Datasets and Embeddings
•Additional Application Components
•RAG
•Model Fine-Tuning
•Securing Generative AI Applications
•Generative AI Application Architecture
•Knowledge Check
•Conclusion
Module 3: Foundation Models
•Introduction to Amazon Bedrock Foundation Models
•Using Amazon Bedrock FMs for Inference
•Amazon Bedrock Methods
•Data Protection and Auditability
•Knowledge Check
•Conclusion
Module 4: Using LangChain
•Optimizing LLM Performance
•Integrating AWS and LangChain
•Using Models with LangChain
•Constructing Prompts
•Structuring Documents with Indexes
•Storing and Retrieving Data with Memory
•Using Chains to Sequence Components
•Managing External Resources with LangChain Agents
•Knowledge Check
•Conclusion
Module 5: Architecture Patterns
•Introduction to Architecture Patterns
•Test Generation and Text Summarization
•Question Answering
•Chatbots
•Code Generation
•LangChain and Amazon Bedrock Agents
•Knowledge Check
•Conclusion
Module 6: Hands-on Labs
•Introduction to Labs
•Lab 1: Performing Text Generation
•Lab 2: Creating Text Summarization
•Lab 3: Using Amazon Bedrock for Question and Answering
•Lab 4: Building a Chatbot
•Lab 5: Using Amazon Bedrock Models for Code Generation
•Lab 6: Integrating Amazon Bedrock Models with LangChain Agents
•Conclusion
Also, Read: Amazon Launched FREE AI Courses & Get Free Certificate: Introduction to Generative Artificial Intelligence
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How To Apply?
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