AI tools for signmaker
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Amazon SageMaker Python SDK
Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.
Staccato
Staccato is an AI-powered music creation and songwriting tool that helps musicians, songwriters, and producers overcome writer's block, generate unique melodies and lyrics, and learn music theory. With its intuitive interface and powerful AI algorithms, Staccato provides a range of features to enhance the creative process, including AI Instrument™ for generating MIDI music, AI Lyrics for creating song lyrics, and educational tools for understanding music theory and songwriting techniques.
amazon-sagemaker-generativeai
Repository for training and deploying Generative AI models, including text-text, text-to-image generation, prompt engineering playground and chain of thought examples using SageMaker Studio. The tool provides a platform for users to experiment with generative AI techniques, enabling them to create text and image outputs based on input data. It offers a range of functionalities for training and deploying models, as well as exploring different generative AI applications.
generative-ai-sagemaker-cdk-demo
This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.
aws-healthcare-lifescience-ai-ml-sample-notebooks
The AWS Healthcare and Life Sciences AI/ML Immersion Day workshops provide hands-on experience for customers to learn about AI/ML services, gain a deep understanding of AWS AI/ML services, and understand best practices for using AI/ML in the context of HCLS applications. The workshops cater to individuals at all levels, from machine learning experts to developers and managers, and cover topics such as training, testing, MLOps, deployment practices, and software development life cycle in the context of AI/ML. The repository contains notebooks that can be used in AWS Instructure-Led Labs or self-paced labs, offering a comprehensive learning experience for integrating AI/ML into applications.
awsome-distributed-training
This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).
guidance-for-a-multi-tenant-generative-ai-gateway-with-cost-and-usage-tracking-on-aws
This repository provides guidance on building a multi-tenant SaaS solution for accessing foundation models using Amazon Bedrock and Amazon SageMaker. It helps enterprise IT teams track usage and costs of foundation models, regulate access, and provide visibility to cost centers. The solution includes an API Gateway design pattern for standardization and governance, enabling loose coupling between model consumers and endpoint services. The CDK Stack deploys resources for private networking, API Gateway, Lambda functions, DynamoDB table, EventBridge, S3 buckets, and Cloudwatch logs.
aws-ai-ml-workshop-kr
AWS AI/ML Workshop & example collection in Korean. The example codes in this repository are divided into 4 categories: AI services, Applied AI, SageMaker, Integration, Generative AI, and AWS Neuron. Each directory has its own Readme file. This repository also provides useful information for self-studying SageMaker.
aws-genai-llm-chatbot
This repository provides code to deploy a chatbot powered by Multi-Model and Multi-RAG using AWS CDK on AWS. Users can experiment with various Large Language Models and Multimodal Language Models from different providers. The solution supports Amazon Bedrock, Amazon SageMaker self-hosted models, and third-party providers via API. It also offers additional resources like AWS Generative AI CDK Constructs and Project Lakechain for building generative AI solutions and document processing. The roadmap and authors are listed, along with contributors. The library is licensed under the MIT-0 License with information on changelog, code of conduct, and contributing guidelines. A legal disclaimer advises users to conduct their own assessment before using the content for production purposes.
amazon-transcribe-live-call-analytics
The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.
generative-ai-cdk-constructs-samples
This repository contains sample applications showcasing the use of AWS Generative AI CDK Constructs to build solutions for document exploration, content generation, image description, and deploying various models on SageMaker. It also includes samples for deploying Amazon Bedrock Agents and automating contract compliance analysis. The samples cover a range of backend and frontend technologies such as TypeScript, Python, and React.
generative-ai-application-builder-on-aws
The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.
litellm
LiteLLM is a tool that allows you to call all LLM APIs using the OpenAI format. This includes Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, and more. LiteLLM manages translating inputs to provider's `completion`, `embedding`, and `image_generation` endpoints, providing consistent output, and retry/fallback logic across multiple deployments. It also supports setting budgets and rate limits per project, api key, and model.
generative-ai-cdk-constructs
The AWS Generative AI Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of AWS Generative AI CDK Constructs is to help developers build generative AI solutions using pattern-based definitions for their architecture. The patterns defined in AWS Generative AI CDK Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.
runhouse
Runhouse is a tool that allows you to build, run, and deploy production-quality AI apps and workflows on your own compute. It provides simple, powerful APIs for the full lifecycle of AI development, from research to evaluation to production to updates to scaling to management, and across any infra. By automatically packaging your apps into scalable, secure, and observable services, Runhouse can also turn otherwise redundant AI activities into common reusable components across your team or company, which improves cost, velocity, and reproducibility.
generative-ai-use-cases-jp
Generative AI (生成 AI) brings revolutionary potential to transform businesses. This repository demonstrates business use cases leveraging Generative AI.
pluto
Pluto is a development tool dedicated to helping developers **build cloud and AI applications more conveniently** , resolving issues such as the challenging deployment of AI applications and open-source models. Developers are able to write applications in familiar programming languages like **Python and TypeScript** , **directly defining and utilizing the cloud resources necessary for the application within their code base** , such as AWS SageMaker, DynamoDB, and more. Pluto automatically deduces the infrastructure resource needs of the app through **static program analysis** and proceeds to create these resources on the specified cloud platform, **simplifying the resources creation and application deployment process**.
llmperf
LLMPerf is a tool designed for evaluating the performance of Language Model APIs. It provides functionalities for conducting load tests to measure inter-token latency and generation throughput, as well as correctness tests to verify the responses. The tool supports various LLM APIs including OpenAI, Anthropic, TogetherAI, Hugging Face, LiteLLM, Vertex AI, and SageMaker. Users can set different parameters for the tests and analyze the results to assess the performance of the LLM APIs. LLMPerf aims to standardize prompts across different APIs and provide consistent evaluation metrics for comparison.
aws-machine-learning-university-responsible-ai
This repository contains slides, notebooks, and data for the Machine Learning University (MLU) Responsible AI class. The mission is to make Machine Learning accessible to everyone, covering widely used ML techniques and applying them to real-world problems. The class includes lectures, final projects, and interactive visuals to help users learn about Responsible AI and core ML concepts.
agent-evaluation
Agent Evaluation is a generative AI-powered framework for testing virtual agents. It implements an LLM agent (evaluator) to orchestrate conversations with your own agent (target) and evaluate responses. It supports popular AWS services, allows concurrent multi-turn conversations, defines hooks for additional tasks, and can be used in CI/CD pipelines for faster delivery and stable production environments.
mlflow
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking <https://mlflow.org/docs/latest/tracking.html>`_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects <https://mlflow.org/docs/latest/projects.html>`_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models <https://mlflow.org/docs/latest/models.html>`_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry <https://mlflow.org/docs/latest/model-registry.html>`_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.
haystack-core-integrations
This repository contains integrations to extend the capabilities of Haystack version 2.0 and onwards. The code in this repo is maintained by deepset, see each integration's `README` file for details around installation, usage and support.