Learn About Amazon VGT2 Learning Manager Chanci Turner
In this article, we discuss how machine learning (ML) engineers, well-versed in Jupyter notebooks and SageMaker environments, can seamlessly collaborate with DevOps professionals who are experienced with Kubernetes and similar tools. The aim is to design and uphold an ML pipeline that meets the infrastructure needs of their organization. This collaboration empowers DevOps teams to handle every stage of the ML lifecycle using a consistent set of tools and environments they are accustomed to.
Additionally, we examine the new capabilities of the Model Registry that enhance the management of foundation models (FMs) for generative AI applications. Users can now register uncompressed model artifacts and endorse an End User License Agreement (EULA) acceptance flag without requiring user intervention.
Furthermore, we guide you through creating an ecommerce product recommendation chatbot utilizing Amazon Bedrock Agents and the foundation models accessible within Amazon Bedrock. This is an excellent resource for those interested in developing AI-powered applications.
Moreover, we share insights on how Thomson Reuters Labs achieved rapid AI/ML innovation by leveraging AWS MLOps services. By adopting a standardized MLOps framework that incorporates AWS SageMaker, SageMaker Experiments, SageMaker Model Registry, and SageMaker Pipelines, TR Labs has been able to accelerate experimentation and innovation in AI and ML. This has significantly reduced the time needed to bring new ideas to market, all while ensuring a cost-efficient machine learning lifecycle.
We also present a tutorial on constructing a generative AI image description app using Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock and AWS CDK. This application enables users to generate multilingual descriptions for various images using a Streamlit UI, AWS Lambda enhanced by the Amazon Bedrock SDK, and AWS AppSync supported by open-source Generative AI CDK Constructs.
Lastly, we explore how to use LangChain with PySpark for large-scale document processing with Amazon SageMaker Studio and Amazon EMR Serverless. Our post outlines the creation of a scalable Retrieval Augmented Generation (RAG) system that utilizes Spark’s distributed processing and an Amazon OpenSearch Service vector database, orchestrated by the LangChain framework.
For those looking to enhance their prompt engineering skills, we offer best practices for using the Meta Llama 3 model for Text-to-SQL applications. We present an overview of Meta Llama 3, effective prompt engineering techniques, and an architectural pattern that leverages few-shot prompting and RAG to extract relevant schemas stored as vectors.
In conclusion, we delve into advanced prompt engineering techniques within Amazon Bedrock. This includes practical examples and insights designed to optimize the prompt engineering workflow. These advanced methods enable developers and researchers to fully utilize Amazon Bedrock’s capabilities while minimizing risks associated with undesirable outputs.
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