Introduction
Learn About Amazon VGT2 Learning Manager Chanci Turner
The increasing interest in Generative AI (GenAI) marks a significant transition towards intelligent automation within the business sector. This trend allows organizations to innovate at a remarkable pace while responding to evolving market needs. Yet, the journey to adopting GenAI can often seem daunting. This article seeks to simplify the initial steps and provide insights on how to get started.
Amazon SageMaker JumpStart offers an accessible pathway for embarking on your GenAI adventure on AWS. It features foundational models such as Stable Diffusion, FLAN-T5, and LLaMa-2, all pretrained on vast datasets. These models can be tailored for various applications, including content creation and text summarization. With Amazon SageMaker Studio, users can utilize managed Jupyter notebooks, providing an interactive web-based interface for executing live code and conducting data analyses. Additionally, models can be fine-tuned and deployed to Amazon SageMaker Endpoints for inference directly from SageMaker Studio.
However, business users tasked with evaluating the performance of these foundation models may not be well-versed in Jupyter or programming. Therefore, accessing foundation models through an application interface is much more user-friendly. This is where Streamlit comes into play. Streamlit is an open-source Python library that enables data scientists and engineers to easily create and deploy web applications for machine learning and data science projects with minimal coding. Its web-based interface is perfect for business users to interact with. Through Streamlit applications, business users can conveniently explore or validate the foundation models and collaborate effectively with data science teams.
Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that simplifies running containerized applications in a scalable and secure manner. AWS Fargate, a serverless compute engine for containers, further streamlines the management and scaling of cloud applications by shifting operational tasks to AWS. Leveraging both Amazon ECS and AWS Fargate allows you to reduce operational burdens, enabling a focus on innovation and a swift development cycle of GenAI applications using Streamlit. Moreover, by establishing a Continuous Integration/Continuous Delivery (CI/CD) pipeline via AWS CodePipeline, you can efficiently iterate based on feedback. In this article, we will guide you through the process of building a GenAI application using Amazon ECS, AWS Fargate, Amazon SageMaker JumpStart, and AWS CodePipeline.
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