community-amp-catalog-default.yaml

name: Community

entries:
  - title: Agentic workflows with AYA (All Your Agents)
    label: agents
    short_description: |
        This AMP implements A.Y.A., an agentic workflow assistant, to demonstrate how agentic workflows can help you interact with a wide and complex series of AI agents and tools.
    long_description: |
        This AMP implements A.Y.A., an agentic workflow assistant, to demonstrate how agentic workflows can help you interact with a wide and complex series of AI agents and tools.  By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.
    image_path: >-
      https://raw.githubusercontent.com/asong-c/CML_AMP_AYA_All_Your_Agents/main/assets/aya-card.png
    tags:
      - Agents
    git_url: 'https://github.com/asong-c/CML_AMP_AYA_All_Your_Agents.git'
    is_prototype: true
    is_community: true
    is_new: true
  - title: Image Analysis with Anthropic's Claude LLM
    label: anthropic-claude
    short_description: |
        This AMP enables transcription and information extraction from images using Anthropic Claude models, covering use cases like text extraction, document QA, and converting unstructured content into structured formats like JSON.
    long_description: |
        This AMP enables transcription and information extraction from images using Anthropic Claude models, covering use cases like text extraction, document QA, and converting unstructured content into structured formats like JSON. IMPORTANT: Please read the following before proceeding.  This AMP includes or otherwise depends on certain third party software packages.  Information about such third party software packages are made available in the notice file associated with this AMP.  By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites.  For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms.
        If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP.  By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.
    image_path: >-
      https://raw.githubusercontent.com/cloudera/CML_AMP_Image-Analysis-with-Anthropic-Claude/main/assets/catalog-entry.png
    tags:
      - Anthropic
      - Claude
      - Image Analysis
      - LLM
      - OCR
      - Transcription
      - Extraction
      - Summarization
    git_url: 'https://github.com/cloudera/CML_AMP_Image-Analysis-with-Anthropic-Claude.git'
    is_prototype: true
    is_community: true
    is_new: true
  - title: Document Summarization with Gemini from Vertex AI
    label: vertex-ai-gemini
    short_description: |
        Summarize documents and text using Google's Gemini models from the Vertex AI Model Garden. 
    long_description: |
        Summarize documents and text using Google's Gemini models from the Vertex AI Model Garden. IMPORTANT: Please read the following before proceeding.  This AMP includes or otherwise depends on certain third party software packages.  Information about such third party software packages are made available in the notice file associated with this AMP.  By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites.  For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms.
        If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP.  By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.
    image_path: >-
      https://raw.githubusercontent.com/cloudera/CML_AMP_Summarization_with_Vertex_AI_Gemini/main/assets/catalog-entry.png
    tags:
      - Gemini
      - Vertex AI
      - Document Summarization
      - LlamaIndex
      - PDF
      - NLP
    git_url: 'https://github.com/cloudera/CML_AMP_Summarization_with_Vertex_AI_Gemini.git'
    is_prototype: true
    is_community: true
    is_new: true
  - title: Document Analysis with Cohere CommandR and FAISS
    label: cohere-chatbot
    short_description: |
        Deploy a chatbot leveraging Cohere and FAISS for PDF Document Analysis
    long_description: |
        Deploy a chatbot leveraging Cohere and FAISS for PDF Document Analysis. IMPORTANT: Please read the following before proceeding.  This AMP includes or otherwise depends on certain third party software packages.  Information about such third party software packages are made available in the notice file associated with this AMP.  By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites.  For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms.
        If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP.  By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.
    image_path: >-
      https://raw.githubusercontent.com/cloudera/CML_AMP-Document-Analysis-with-Cohere-CommandR-and-FAISS/main/images/catalog-entry.jpg
    tags:
      - Chatbot
      - Cohere
      - FAISS
      - LLM
      - Generative AI
      - RAG
      - NLP
      - PDF
    git_url: 'https://github.com/cloudera/CML_AMP-Document-Analysis-with-Cohere-CommandR-and-FAISS.git'
    is_prototype: true
    is_community: true
    is_new: true
  - title: DocGenius AI - Generative AI Chatbot Powered by Cloudera
    label: doc-genius-ai
    short_description: Generative AI Chatbot for your Documents - Built by Cloudera Professional Services.
    long_description: |
      Generative AI Chatbot for your Documents powered by Cloudera - Built by Cloudera Professional Services.
      
      ---------------------------
      IMPORTANT: Please read the following before proceeding. This AMP includes or otherwise depends on certain third party software packages. Information about such third party software packages are made available in the notice file associated with this AMP. By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites. For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms.

      -----
      
      If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP. By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.

      Copyright (c) 2024 - Cloudera, Inc. All rights reserved.
      ---------------------------
    long_description_html: |
      Generative AI Chatbot for your Documents powered by Cloudera.
      <div style="margin-top:10px"><b>IMPORTANT:</b> Please read the following before proceeding.</div>
      <div style="margin-top:10px"> This AMP includes or otherwise depends on certain third party software packages. Information about such third party software packages are made available in the notice file associated with this AMP. By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites. For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms.</div>
      <div style="margin-top:10px"><b> If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP. By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.</b></div>
      <div style="margin-top:10px"><b> Copyright (c) 2024 - Cloudera, Inc. All rights reserved.</b></div>
    image_path: >-
      https://raw.githubusercontent.com/thammuio/doc-genius-ai/main/images/doc-genius-ai.png
    tags:
      - Professional Services
      - Cloudera AI
      - Generative AI
      - RAG Pipelines
      - LLMOps
      - Chatbot
      - AI Agents
    git_url: "https://github.com/thammuio/doc-genius-ai.git"
    is_prototype: true
    is_community: true
    is_new: true
  - title: Multi-Agent API Orchestrator using CrewAI
    label: multiagent
    short_description: Talk to your APIs using a multi-agent system powered by CrewAI
    long_description: >-
       This project highlights the impressive capabilities of multi-agent systems with the CrewAI framework. It enables users to provide API specifications and interact with their services using natural language, illustrating the potential of these systems to automate and streamline complex workflows. 
    image_path: >-
      https://raw.githubusercontent.com/pranav-bhatt/ai_agents/main/assets/amp_splash_image.png
    tags:
      - MultiAgents
      - crewai
    git_url: "https://github.com/pranav-bhatt/ai_agents.git" 
    is_prototype: true
    is_community: true
    is_new: true
  - title: Contextual Chatbot with NeMo Guardrails
    label: CML_AMP_NeMo-Guardrails-Chatbot
    short_description: |
        This Applied Machine Learning Prototype (AMP) is a similarity-search chatbot that demonstrates safe and responsible AI use for organizations through customizable guardrails.
    long_description: |
        This Applied Machine Learning Prototype (AMP) builds a similarity-search based chatbot built using Langchain, OpenAI embeddings, Pinecone Vector DB, and NeMo-Guardrails. This chatbot is designed to showcase how organizations can leverage AI safely and responsibly by implementing guardrails.
    long_description_html: |
        This Applied Machine Learning Prototype (AMP) builds a similarity-search based chatbot built using Langchain, OpenAI embeddings, Pinecone Vector DB, and NeMo-Guardrails. This chatbot is designed to showcase how organizations can leverage AI safely and responsibly by implementing guardrails.
    image_path: >-
      https://raw.githubusercontent.com/kevinbtalbert/CML_AMP_NeMo-Guardrails-Chatbot/main/assets/demo.png
    tags:
      - NeMo Guardrails
      - Secure AI
      - OpenAI
      - Langchain
      - Pinecone
      - Streamlit
      - NVIDIA Rails
      - Chatbot
    git_url: 'https://github.com/kevinbtalbert/CML_AMP_NeMo-Guardrails-Chatbot.git'
    is_prototype: true
    is_community: true
    is_new: true
  - title: CML HuggingFace Models
    label: cml_hf_models
    short_description: |
        Choose any 7B or 13B LLM from HuggingFace and deploy as a CML Model.
    long_description: |
        Choose any 7B or 13B LLM from HuggingFace and deploy as a CML Model. Cloudera Machine Learning models expose an Inference endpoint for users to access and communicate with. The AMP creates a Gradio App UI which can be used to interact with the deployed CML Model.
    long_description_html: |
        Choose any 7B or 13B LLM from HuggingFace and deploy as a CML Model. Cloudera Machine Learning models expose an Inference endpoint for users to access and communicate with. The AMP creates a Gradio App UI which can be used to interact with the deployed CML Model.
    image_path: >-
      https://raw.githubusercontent.com/nkityd09/cml_hf_models/main/images/cml_hf_ui.png
    tags:
      - huggingface
      - 7B
      - 13V
    git_url: 'https://github.com/nkityd09/cml_hf_models.git'
    is_prototype: true
    is_community: true
    is_new: false
  - title: Text to Image Using Stable Diffusion
    label: CML_AMP-Text-to-Image-with-Stable-Diffusion
    short_description: |
        Run a browser interface based on Gradio library for Stable Diffusion within the CML platform.
    long_description: |
        Run a browser interface based on Gradio library for Stable Diffusion within the CML platform.
    long_description_html: |
        Run a browser interface based on Gradio library for Stable Diffusion within the CML platform.
    image_path: >-
      https://raw.githubusercontent.com/kevinbtalbert/CML_AMP-Text-to-Image-with-Stable-Diffusion/master/catalog-entry.png
    tags:
      - Text2Image
      - Stable Diffusion
    git_url: 'https://github.com/kevinbtalbert/CML_AMP-Text-to-Image-with-Stable-Diffusion.git'
    is_prototype: true
    is_community: true
    is_new: false
  - title: Text Summarization using IBM watsonx.ai
    label: CML_AMP_watsonxai
    short_description: |
        This repository demonstrates how to use watson machine learning Python SDK to call watsonx.ai models from Cloudera Machine Learning (CML) workspace.
    long_description: |
        This repository demonstrates how to use watson machine learning Python SDK to call watsonx.ai models from Cloudera Machine Learning (CML) workspace.
    long_description_html: |
        This repository demonstrates how to use watson machine learning Python SDK to call watsonx.ai models from Cloudera Machine Learning (CML) workspace.
    image_path: >-
      https://raw.githubusercontent.com/agupta-git/CML_AMP_watsonxai/main/assets/app_interface.png
    tags:
      - watsonx
      - Text Summarization
      - IBM
    git_url: 'https://github.com/agupta-git/CML_AMP_watsonxai.git'
    is_prototype: true
    is_community: true
    is_new: false
  - title: Ray on CML QuickStart
    label: ray
    short_description: A series of starter notebooks that demonstrate how to use Ray on CML
    long_description: >-
      A series of starter notebooks that demonstrate how to launch a Ray cluster, use python libraries, train and deploy a model using Ray Tune in CML.
    image_path: >-
      https://github.com/vidushisomani/CML_Ray_Starter_AMP/blob/main/images/amp-cover.png?raw=true
    tags:
      - Ray
    git_url: "https://github.com/vidushisomani/CML_Ray_Starter_AMP.git" 
    is_prototype: true
    is_community: true
    is_new: false
  - title: Solr 9
    label: CML_AMP_solr-runtime
    short_description: |
      Run Solr 9 as an Application within an AMP. Installs necessary Solr and Java runtime components.
    long_description: |
      Run Solr 9 as an Application within an AMP. Installs necessary Solr and Java runtime components. By default, Solr 9.3.0 and Java 11.0.1 will be used.
    image_path: "https://raw.githubusercontent.com/kevinbtalbert/CML_AMP_Solr_9/main/assets/amp-cover.png"
    tags: 
      - Solr 9
      - Vector DB
    git_url: "https://github.com/kevinbtalbert/CML_AMP_Solr_9.git"
    is_prototype: true
    is_community: true
    is_new: false
  - title: Mistral 7B CML-Hosted Model
    label: llm-model-deploy
    short_description: |
      This AMP deploys Mistral-7B model as a CML API endpoint. The "Ephemeral Storage Limit" in Site Administration must be set to 20GB or greater before deploying this AMP.
    long_description: |
      This AMP deploys Mistral-7B model as a CML API endpoint. Requires a GPU node with 4 vCores and 16 GB memory minimum. Note, you will need to ensure the "Ephemeral Storage Limit" in Site Administration is set to 20GB or greater for the model to successfully deploy. IMPORTANT: Please read the following before proceeding.  This AMP includes or otherwise depends on certain third party software packages.  Information about such third party software packages are made available in the notice file associated with this AMP.  By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties’ websites.  For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms. If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP.  By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.
    image_path: "https://raw.githubusercontent.com/cloudera/CML_AMP_Deploy-Mistral7B-CML-Native-Model/main/images/catalog-entry.png"
    tags: 
      - Mistral 7B
      - LLM
      - CML Labs
      - Model Deployment
      - GPU
    git_url: "https://github.com/cloudera/CML_AMP_Deploy-Mistral7B-CML-Native-Model.git"
    is_prototype: true
    is_community: true
    is_new: false
  - title: AviWind Guardian
    label: AviWindGuardian
    short_description: 2024 Climate and Sustainability Hackathon with AMD 1st Place - AviWind Guardian aims to leverage machine learning and data analytics to predict and mitigate the impacts of wind turbines on migratory birds.
    long_description: >-
      As the world increasingly turns to renewable energy to address the climate crisis, the expansion of wind power poses significant ecological challenges, particularly for migratory birds. The growth of wind farms leads to bird collisions with turbines and habitat disruptions, threatening species and ecological balance. The placement of wind farms often results in habitat loss, affecting essential ecological processes like pollination and seed dispersal. Addressing these issues, AviWind Guardian harnesses machine learning and data analytics to predict and mitigate impacts on migratory birds, aiding developers and conservationists in fostering a balance between advancing renewable energy and preserving avian life and ecological equilibrium..
    tags:
      - WindGuardian
    git_url: "https://github.com/trueblood/AviWindGuardian.git"
    git_ref: "80b56a44a60d0de586bc7e1cc7bd093fe5a402fa"
    is_prototype: true
    is_community: true
    is_new: false
  - title: CECALT Hurricane Behavior Predictor
    label: CECALT
    short_description: 2024 Climate and Sustainability Hackathon with AMD 2nd Place - Objective of demonstrating that the developed model can improve the reliability of wind speed prediction in hurricanes, taking into account several novel factors
    long_description: >-
      The CECALT Hurricane Behavior Predictor project has developed a model to enhance wind speed prediction reliability during hurricanes by considering variables such as peak wind speed, geographical coordinates, atmospheric conditions, and changes in wind speed. The model is integrated into an application with a user-friendly interface that allows for real-time input of hurricane data. Once processed, the application displays the estimated wind speed and the hurricane's location on a map, leveraging Cloudera’s robust infrastructure and machine learning capabilities. This setup promises to facilitate proactive decision-making and timely responses to changing hurricane intensities.
    tags:
      - CECALT
      - HurricanePredictor
    git_url: "https://github.com/amcm329/cod_hurricane_prediction.git"
    git_ref: "efcf5c5adf629b675904103daf7f41e782e8665f"
    is_prototype: true
    is_community: true
    is_new: false
  - title: Climate Change Impact on the Himalayan Timberline
    label: HimalayanTimberline
    short_description: 2024 Climate and Sustainability Hackathon with AMD 3rd Place - Assessing the Impact of Climate Change on Himalayan Timberline Elevation - Developing a Novel Tool for Analyzing Correlation with Temperature and Precipitation
    long_description: >-
      The Himalayan timberline, marking the upper tree growth limit in this high mountain range, is crucial for stabilizing mountain slopes, regulating water flow, and providing wildlife habitat. Positioned between 3,500 and 4,500 meters, it features a range of plant species adapted to severe conditions like extreme cold, high winds, and low oxygen. However, this sensitive ecological zone faces threats from climate change, evidenced by rapid warming trends that outpace global averages, especially in the Trans-Himalayan cold deserts. These changes, including altered precipitation patterns and temperature gradients, endanger the timberline's stability, water regulatory roles, and biodiversity. Addressing these challenges requires a comprehensive understanding of how climatic variables like temperature and precipitation impact the timberline ecosystem, informing targeted mitigation and adaptation strategies to preserve this fragile ecological balance for future generations.
    tags:
      - Himalayan
      - Timberline
    git_url: "https://github.com/Lokeshiiith/Cloudera_project.git" 
    git_ref: "efcf5c5adf629b675904103daf7f41e782e8665f"
    is_prototype: true
    is_community: true
    is_new: false