Phase 1 of Huggingface datasets library to SingularityNET Pipeline

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Anthony Oliko
Project Owner

Phase 1 of Huggingface datasets library to SingularityNET Pipeline

Funding Awarded

$19,500 USD

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Status

  • Overall Status

    🛠️ In Progress

  • Funding Transfered

    $16,000 USD

  • Max Funding Amount

    $19,500 USD

Funding Schedule

View Milestones
Milestone Release 1
$1,500 USD Transfer Complete 29 Dec 2023
Milestone Release 2
$2,000 USD Transfer Complete 29 Dec 2023
Milestone Release 3
$2,500 USD Transfer Complete 05 Jan 2024
Milestone Release 4
$2,000 USD Transfer Complete 12 Jan 2024
Milestone Release 5
$2,000 USD Transfer Complete 19 Jan 2024
Milestone Release 6
$2,500 USD Transfer Complete 26 Jan 2024
Milestone Release 7
$2,000 USD Transfer Complete 26 Jan 2024
Milestone Release 8
$2,500 USD Pending TBD
Milestone Release 9
$1,500 USD Transfer Complete 01 Mar 2024
Milestone Release 10
$1,000 USD Pending TBD

Project AI Services

No Service Available

Overview

Phase 1: Building the Hugging Face Datasets library to SingularityNET Pipeline Framework

In this phase, we propose to build the foundational framework for the Hugging Face Datasets library to the SingularityNET Pipeline. This pipeline will facilitate seamless integration and accessibility of Knowledge Graph Question Answer datasets via the Hugging Face Datasets library, providing an efficient interface for users to access and leverage these datasets for future AGI Integrations. The framework will serve as the backbone for the subsequent integration of specific datasets in Phase 2.

 

Proposal Description

Compnay Name

Trenches AI

Solution Description

In response to the Request for Proposal (RFP), we entered Phase 1 of our solution under the RFP4 Tools For Knowledge Graphs And LLMs Integration because it involves building a pipeline of queryable endpoints for uniformly assessing, downloading, and querying knowledge graphs through the Higgingface dataset Python library. This solution aligns perfectly with the RFP4 objectives and requirements.

Using the endpoints built in the pipeline, Users, the SingularityNET Tech Team, and AGI of the future can easily, assess Hugginface datasets without previous experience using Huggingface or Knowledge graphs.

Pipeline Architecture:

A pipeline architecture that seamlessly provides a unified way to access the Hugging Face Datasets library. Using a framework in which components for data retrieval, transformation, and integration into the SingularityNET platform will be created, and from which Users, LLMs, and AGI can assess the Knowledge Graphs.

 

 

Data Retrieval Component:

  • The data retrieval component will leverage the Hugging Face Datasets library. This component will include functions or classes responsible for fetching datasets from the Hugging Face repository.
  • A caching mechanism to reduce redundant data fetching and improve performance.
  • Error handling for cases where datasets may not be available or accessible.

Data Transformation Component:

  • A data transformation module is responsible for converting the retrieved datasets into a format compatible with SingularityNET's requirements.
  • Implement schema mapping to adapt datasets to the expected data structure of the platform.
  • Handle any necessary data preprocessing, such as cleaning and normalization.

Deployment:

  • AWS.

Justification of Uniqueness:

The uniqueness of this project lies in its ability to bridge the gap between state-of-the-art NLP datasets available through the Hugging Face Datasets library and the SingularityNET platform's capabilities. By creating a dedicated pipeline, we will enable easy access to valuable knowledge graph question-answer datasets, which is a crucial resource for AGI research and development. This pipeline will provide SingularityNET with a distinctive toolset for advancing its platform strategy and preparing for the AGI revolution. and will be useful for future research and/or development of KG-powered LLMs.

Project Benefits for SNET AI platform

Impact on SingularityNET:

  • Enhanced Data Resources: The library will provide SNET with a rich and diverse collection of Knowledge Graph Question Answer datasets.
  • Facilitate Research and Development: Access to high-quality datasets through a unified pipeline will accelerate research and development efforts within SNET. Researchers and developers can easily tap into these datasets to train and evaluate large language models, facilitating the advancement of AI technologies.
  • Alignment with AGI Strategy: The Pipeline aligns with SNET's strategy to prepare for the AGI (Artificial General Intelligence) revolution. It supports the development of Neuro Symbolic Large Language Models and fosters a collaborative environment for decentralized AGI research.
  • Motivation for AGI Research: By integrating these datasets, SNET provides motivation for future AGI research. Researchers can use the library as a valuable resource to benchmark and test their AGI models, contributing to the broader AGI community.

Impact on the Community:

  • Accessible Knowledge Graphs: The library democratizes access to Knowledge Graph Question Answer datasets for the wider AI and research community. This availability fosters innovation by lowering barriers to entry and enabling more individuals and organizations to leverage these resources.
  • Advanced AI Solutions: Researchers, developers, and data scientists can leverage the library to build advanced AI solutions, including chatbots, virtual assistants, and knowledge-based systems that utilize the power of Knowledge Graphs.
  • Collaborative Ecosystem: The integration of the library into SNET encourages collaboration within the AI community. Researchers and developers from different backgrounds can work together on AGI-related projects and share insights and findings.
  • Educational Resource: The library can serve as an educational resource, allowing students and educators to access real-world datasets for AI and NLP research and coursework.

In summary, the Hugging Face Datasets Library with specific datasets integrated into SingularityNET not only enhances the platform's capabilities but also contributes to the broader AI and AGI research community. It accelerates research, fosters collaboration, and provides valuable resources for building advanced AI solutions, aligning with the goals of advancing AI towards AGI.

 

Milestone & Budget

Milestone 1: R&D, Project / Framework Setup (Cost: $1,500)

Duration: 1 week

Tasks:

  • Research & Development
  • Select and set up the Python web framework.
  • Configure the AWS environment, VCP instances, and necessary infrastructure.
  • Initialize the version control system and create the project repository.

Milestone 2: Modular Architecture Design and Data Retrieval (Cost: $2,000)

Duration: 2 weeks

Tasks:

  • Design the modular architecture of the pipeline.
  • Develop the data retrieval component.
  • Implement basic error handling and logging for data retrieval.

Milestone 3: Data Transformation and Schema Mapping (Cost: $2,500)

Duration: 3 weeks

Tasks:

  • Build the data transformation module.
  • Create schema mapping functions for dataset adaptation.
  • Perform data preprocessing as needed.
  • Implement data persistence and indexing
  • Integrate the Time Questions dataset from Huggingface.

Milestone 4: Web API Development (Cost: $2,000)

Duration: 2 weeks

Tasks:

  • Develop an API for operating the system.
  • Implement route handlers and controllers.
  • Integrate data retrieval and transformation components into the API.
  • Implement endpoint access management.

Milestone 5: Security and Access Control (Cost: $2,000)

Duration: 1 week

Tasks:

  • Enhance security with custom Auth or other authentication methods.
  • Implement access controls to restrict unauthorized access.
  • Conduct security testing and vulnerability assessment.

Milestone 6: Documentation and Testing (Cost: $2,500)

Duration: 3 weeks

Tasks:

  • Comprehensive API documentation.
  • Develop unit tests and integration tests.
  • Test various scenarios, including error handling.
  • Set up a documentation website resource.

Milestone 7: Cloud Deployment (Cost: $2,000)

Duration: 2 weeks

Tasks:

  • Configure AWS services, including ECS, RDS (if needed), and S3 for static assets.
  • Set up load balancing and auto-scaling for scalability.
  • Setup CI/CD pipeline
  • Deploy the pipeline on AWS infrastructure.

Milestone 8: SingularityNET Alignment (Cost: $2,500)

Duration: 3 weeks

Tasks:

  • Ensure compatibility with SingularityNET specifications.
  • Establish communication channels or APIs for integration.
  • Conduct integration testing with SingularityNET components.

Milestone 9: Performance Optimization and Monitoring (Cost: $1,500)

Duration: 1 week

Tasks:

  • Implement performance optimizations, such as asynchronous processing and Rate Limiting.
  • Fine-tune AWS infrastructure for cost efficiency.
  • Set up comprehensive monitoring and alerting.

Milestone 10: Final Testing and Deployment (Cost: $1,000)

Duration: 1 weeks

Tasks:

  • Conduct a final round of testing, including load testing.
  • Address any remaining issues or bugs.
  • Deploy the final, production-ready pipeline.

Deliverables

Deliverables 1:

  • Completed research and evaluation of the selected Python web framework.
  • AWS environment setup, including VPC instances and required infrastructure configurations.
  • A project repository initialized with a version control system (Git).
  • Documentation outlining the chosen framework, AWS setup, and repository structure.

Deliverables 2:

  • Detailed modular architecture design document specifying the components and their interactions.
  • Functional data retrieval component capable of fetching datasets from Hugging Face Datasets library.
  • Basic error handling and logging mechanisms implemented within the data retrieval component.
  • Documentation of the data retrieval component's usage and design.

Deliverables 3:

  • Functional data transformation module capable of converting the retrieved datasets.
  • Schema mapping functions for adapting datasets to Knowledge Graph / SingularityNET's requirements.
  • Data preprocessing routines as needed for clean and structured data.
  • Data persistence mechanisms and indexing strategies documented.

Deliverables 4:

  • An operational API for interacting with the system.
  • Route handlers and controllers to handle API requests and data processing.
  • Successful integration of data retrieval and transformation components into the API.
  • Implementation of endpoint access management and security measures.
  • Comprehensive API documentation describing available endpoints and usage.

Deliverables 5:

  • Enhanced security measures, including custom authentication methods (if applicable).
  • Access control mechanisms implemented to restrict unauthorized access.
  • Documentation of security measures, including security testing and vulnerability assessment reports.

Deliverables 6:

  • Comprehensive API documentation covering endpoint descriptions, usage examples, and data schemas.
  • A documentation website resource accessible to users and developers.

Deliverables 7:

  • AWS services configured, including EC2, RDS (if used), and S3 for static assets.
  • Load balancing and auto-scaling configurations set up for scalability.
  • The pipeline successfully deployed on AWS infrastructure.

Deliverables 8:

  • The pipeline confirmed for compatibility with SingularityNET specifications.
  • Established communication channels or APIs for seamless integration.
  • Integration testing conducted with SingularityNET components, including documented results.

Deliverables 9:

  • Implemented performance optimizations, such as asynchronous processing and rate limiting.
  • AWS infrastructure fine-tuned for cost efficiency.
  • Comprehensive monitoring and alerting system configured and documented.

Deliverables 9:

  • Successful completion of a final round of testing, including load testing results.
  • Addressed any remaining issues or bugs identified during testing.
  • Deployment of the final, production-ready pipeline to the specified environment.

Achieving Goals

Risk: Insufficient resources, including budget, time, or skilled personnel, can hinder project progress.

Mitigation: Continuously monitor resource allocation. If additional resources are required, request them early and justify their necessity. Consider resource scaling options on AWS for flexibility.

5. Alignment Challenges with SingularityNET:

Risk: Aligning the pipeline with SingularityNET may pose unforeseen challenges due to differences in architecture or communication protocols.

Mitigation: Collaborate closely with SingularityNET's technical team. Conduct thorough integration testing and ensure compatibility by following SingularityNET's guidelines and best practices.

6. Legal and Intellectual Property Issues:

Risk: Legal issues related to intellectual property, licensing, or data ownership could arise.

Mitigation: Conduct a thorough review of licenses for libraries and datasets used. Ensure compliance with relevant intellectual property laws and contracts. Seek legal advice when necessary.

Competitive Landscape

1. Technical Challenges:

Risk: The project may face technical difficulties in implementing specific components of the pipeline or integrating with SingularityNET.

Mitigation: Conduct thorough research and prototyping before full implementation. Seek advice from experts if necessary. Allocate extra time and resources for handling unforeseen technical challenges.

2. Scope Creep:

Risk: There is a risk of expanding the project scope beyond its initial plan, leading to delays and budget overruns.

Mitigation: Define a clear project scope and stick to it. Any proposed scope changes should go through a formal change request process and be evaluated for their impact on timelines and costs.

3. Security Vulnerabilities:

Risk: Security vulnerabilities, such as API vulnerabilities or data breaches, can compromise the integrity of the pipeline.

Mitigation: Implement security best practices, conduct security audits, and stay updated on the latest security threats. Regularly test the pipeline for vulnerabilities and apply security patches promptly.

4. Resource Constraints:

Risk: Insufficient resources, including budget, time, or skilled personnel, can hinder project progress.

Mitigation: Continuously monitor resource allocation. If additional resources are required, request them early and justify their necessity. Consider resource scaling options on AWS for flexibility.

5. Alignment Challenges with SingularityNET:

Risk: Aligning the pipeline with SingularityNET may pose unforeseen challenges due to differences in architecture or communication protocols.

Mitigation: Collaborate closely with SingularityNET's technical team. Conduct thorough integration testing and ensure compatibility by following SingularityNET's guidelines and best practices.

6. Legal and Intellectual Property Issues:

Risk: Legal issues related to intellectual property, licensing, or data ownership could arise.

Mitigation: Conduct a thorough review of licenses for libraries and datasets used. Ensure compliance with relevant intellectual property laws and contracts. Seek legal advice when necessary.

Long Description

Company Name

Trenches AI

Proposal Pool

RFP4

Tools For Knowledge Graphs And LLMs Integration

Summary

Phase 1: Building the Hugging Face Datasets library to SingularityNET Pipeline Framework

In this phase, we propose to build the foundational framework for the Hugging Face Datasets library to SingularityNET Pipeline. This pipeline will facilitate seamless integration and accessibility of Knowledge Graph Question Answer datasets via the Hugging Face Datasets library, providing an efficient interface for users to access and leverage these datasets for future AGI Integrations. The framework will serve as the backbone for the subsequent integration of specific datasets in Phase 2.

Funding Amount

Phase 1 Budget: $19,500

Knowledge Graph Question Answer (KGQA) datasets are collections of questions and answers that are linked to a knowledge graph, which represents entities and their relationships. These datasets are used to train and evaluate question-answering systems that can handle complex, open-domain questions by leveraging the structured knowledge in the graph. Examples of KGQA datasets include Google's Knowledge Vault and Wikidata.

-

🤗 Hugging Face Datasets is a library for easily accessing and sharing datasets for Audio, Computer Vision, and Natural Language Processing (NLP) tasks.

-

Our Solution:

In response to the Request for Proposal (RFP), we entered Phase 1 of our solution under the RFP4 Tools For Knowledge Graphs And LLMs Integration because it involves building a pipeline of queryable endpoints for uniformly assessing, downloading, and querying knowledge graphs through the Higgingface dataset Python library. This solution aligns perfectly with the RFP4 objectives and requirements.

Using the endpoints built in the pipeline, Users, the SingularityNET Tech Team, and AGI of the future can easily, assess Hugginface datasets without previous experience using Huggingface or Knowledge graphs.

Pipeline Architecture:

A pipeline architecture that seamlessly provides a unified way to access the Hugging Face Datasets library. Using a framework in which components for data retrieval, transformation, and integration into the SingularityNET platform will be created, and from which Users, LLMs, and AGI can assess the Knowledge Graphs.

 

 

Data Retrieval Component:

  • The data retrieval component will leverage the Hugging Face Datasets library. This component will include functions or classes responsible for fetching datasets from the Hugging Face repository.
  • A caching mechanism to reduce redundant data fetching and improve performance.
  • Error handling for cases where datasets may not be available or accessible.

Data Transformation Component:

  • A data transformation module is responsible for converting the retrieved datasets into a format compatible with SingularityNET's requirements.
  • Implement schema mapping to adapt datasets to the expected data structure of the platform.
  • Handle any necessary data preprocessing, such as cleaning and normalization.

Deployment:

  • AWS.

Justification of Uniqueness:

The uniqueness of this project lies in its ability to bridge the gap between state-of-the-art NLP datasets available through the Hugging Face Datasets library and the SingularityNET platform's capabilities. By creating a dedicated pipeline, we will enable easy access to valuable knowledge graph question-answer datasets, which is a crucial resource for AGI research and development. This pipeline will provide SingularityNET with a distinctive toolset for advancing its platform strategy and preparing for the AGI revolution. and will be useful for future research and/or development of KG-powered LLMs.

Impact on SingularityNET:

  • Enhanced Data Resources: The library will provide SNET with a rich and diverse collection of Knowledge Graph Question Answer datasets.
  • Facilitate Research and Development: Access to high-quality datasets through a unified pipeline will accelerate research and development efforts within SNET. Researchers and developers can easily tap into these datasets to train and evaluate large language models, facilitating the advancement of AI technologies.
  • Alignment with AGI Strategy: The Pipeline aligns with SNET's strategy to prepare for the AGI (Artificial General Intelligence) revolution. It supports the development of Neuro Symbolic Large Language Models and fosters a collaborative environment for decentralized AGI research.
  • Motivation for AGI Research: By integrating these datasets, SNET provides motivation for future AGI research. Researchers can use the library as a valuable resource to benchmark and test their AGI models, contributing to the broader AGI community.

Impact on the Community:

  • Accessible Knowledge Graphs: The library democratizes access to Knowledge Graph Question Answer datasets for the wider AI and research community. This availability fosters innovation by lowering barriers to entry and enabling more individuals and organizations to leverage these resources.
  • Advanced AI Solutions: Researchers, developers, and data scientists can leverage the library to build advanced AI solutions, including chatbots, virtual assistants, and knowledge-based systems that utilize the power of Knowledge Graphs.
  • Collaborative Ecosystem: The integration of the library into SNET encourages collaboration within the AI community. Researchers and developers from different backgrounds can work together on AGI-related projects and share insights and findings.
  • Educational Resource: The library can serve as an educational resource, allowing students and educators to access real-world datasets for AI and NLP research and coursework.

In summary, the Hugging Face Datasets Library with specific datasets integrated into SingularityNET not only enhances the platform's capabilities but also contributes to the broader AI and AGI research community. It accelerates research, fosters collaboration, and provides valuable resources for building advanced AI solutions, aligning with the goals of advancing AI towards AGI.

Project Milestones and Cost Breakdown

Milestone 1: R&D, Project / Framework Setup (Cost: $1,500)

Duration: 1 week

Tasks:

  • Research & Development
  • Select and set up the Python web framework.
  • Configure the AWS environment, VCP instances, and necessary infrastructure.
  • Initialize the version control system and create the project repository.

Deliverables:

  • Completed research and evaluation of the selected Python web framework.
  • AWS environment setup, including VPC instances and required infrastructure configurations.
  • A project repository initialized with a version control system (Git).
  • Documentation outlining the chosen framework, AWS setup, and repository structure.

Milestone 2: Modular Architecture Design and Data Retrieval (Cost: $2,000)

Duration: 2 weeks

Tasks:

  • Design the modular architecture of the pipeline.
  • Develop the data retrieval component.
  • Implement basic error handling and logging for data retrieval.

Deliverables:

  • Detailed modular architecture design document specifying the components and their interactions.
  • Functional data retrieval component capable of fetching datasets from Hugging Face Datasets library.
  • Basic error handling and logging mechanisms implemented within the data retrieval component.
  • Documentation of the data retrieval component's usage and design.

Milestone 3: Data Transformation and Schema Mapping (Cost: $2,500)

Duration: 3 weeks

Tasks:

  • Build the data transformation module.
  • Create schema mapping functions for dataset adaptation.
  • Perform data preprocessing as needed.
  • Implement data persistence and indexing
  • Integrate the Time Questions dataset from Huggingface.

Deliverables:

  • Functional data transformation module capable of converting the retrieved datasets.
  • Schema mapping functions for adapting datasets to Knowledge Graph / SingularityNET's requirements.
  • Data preprocessing routines as needed for clean and structured data.
  • Data persistence mechanisms and indexing strategies documented.

Milestone 4: Web API Development (Cost: $2,000)

Duration: 2 weeks

Tasks:

  • Develop an API for operating the system.
  • Implement route handlers and controllers.
  • Integrate data retrieval and transformation components into the API.
  • Implement endpoint access management.

Deliverables:

  • An operational API for interacting with the system.
  • Route handlers and controllers to handle API requests and data processing.
  • Successful integration of data retrieval and transformation components into the API.
  • Implementation of endpoint access management and security measures.
  • Comprehensive API documentation describing available endpoints and usage.

Milestone 5: Security and Access Control (Cost: $2,000)

Duration: 1 week

Tasks:

  • Enhance security with custom Auth or other authentication methods.
  • Implement access controls to restrict unauthorized access.
  • Conduct security testing and vulnerability assessment.

Deliverables:

  • Enhanced security measures, including custom authentication methods (if applicable).
  • Access control mechanisms implemented to restrict unauthorized access.
  • Documentation of security measures, including security testing and vulnerability assessment reports.

Milestone 6: Documentation and Testing (Cost: $2,500)

Duration: 3 weeks

Tasks:

  • Comprehensive API documentation.
  • Develop unit tests and integration tests.
  • Test various scenarios, including error handling.
  • Set up a documentation website resource.

Deliverables:

  • Comprehensive API documentation covering endpoint descriptions, usage examples, and data schemas.
  • A documentation website resource accessible to users and developers.

Milestone 7: Cloud Deployment (Cost: $2,000)

Duration: 2 weeks

Tasks:

  • Configure AWS services, including ECS, RDS (if needed), and S3 for static assets.
  • Set up load balancing and auto-scaling for scalability.
  • Setup CI/CD pipeline
  • Deploy the pipeline on AWS infrastructure.

Deliverables:

  • AWS services configured, including EC2, RDS (if used), and S3 for static assets.
  • Load balancing and auto-scaling configurations set up for scalability.
  • The pipeline successfully deployed on AWS infrastructure.

Milestone 8: SingularityNET Alignment (Cost: $2,500)

Duration: 3 weeks

Tasks:

  • Ensure compatibility with SingularityNET specifications.
  • Establish communication channels or APIs for integration.
  • Conduct integration testing with SingularityNET components.

Deliverables:

  • The pipeline confirmed for compatibility with SingularityNET specifications.
  • Established communication channels or APIs for seamless integration.
  • Integration testing conducted with SingularityNET components, including documented results.

Milestone 9: Performance Optimization and Monitoring (Cost: $1,500)

Duration: 1 week

Tasks:

  • Implement performance optimizations, such as asynchronous processing and Rate Limiting.
  • Fine-tune AWS infrastructure for cost efficiency.
  • Set up comprehensive monitoring and alerting.

Deliverables:

  • Implemented performance optimizations, such as asynchronous processing and rate limiting.
  • AWS infrastructure fine-tuned for cost efficiency.
  • Comprehensive monitoring and alerting system configured and documented.

Milestone 10: Final Testing and Deployment (Cost: $1,000)

Duration: 1 weeks

Tasks:

  • Conduct a final round of testing, including load testing.
  • Address any remaining issues or bugs.
  • Deploy the final, production-ready pipeline.

Deliverables:

  • Successful completion of a final round of testing, including load testing results.
  • Addressed any remaining issues or bugs identified during testing.
  • Deployment of the final, production-ready pipeline to the specified environment.

Total Budget: $19,500

Phase 2: Integration of 5 Knowledge Graph Question Answer Datasets

In Phase 2, we aim to integrate five specific Knowledge Graph Question Answer datasets into the Pipeline, thus expanding the repository and enhancing its utility. These datasets will be selected based on their relevance to AGI research and specific use-cases.

Our Team

Anthony Oliko, CTO

Anthony is an experienced programmer, with a specialty in building and deploying scalable web applications and services. His duties will include backend integrations of the required libraries and also data manipulation, conversion, and storage, as well as building out the programming interfaces for the solution.

 

Andria Ezetendu, Data Scientist

Andria is a Senior Data Scientist with key competencies in data analysis and visualization, business intelligence analysis, and Agile project management. Her wealth of experience will come into play when converting web algorithms to useful products.

Abdulmalik Ibrahim, DevOps

Malik is a versatile and self-driven DevOps Engineer with 4+ years of professional experience in IT comprising database administration, software development, design and implementation, Application Monitoring, Observability and Application Performance Management (APM), Integration of DevOps tools on Cloud platforms and On-Premise environment, Containerization of applications, Build and Release automation with Cloud Services.

Risk and Mitigation

1. Technical Challenges:

Risk: The project may face technical difficulties in implementing specific components of the pipeline or integrating with SingularityNET.

Mitigation: Conduct thorough research and prototyping before full implementation. Seek advice from experts if necessary. Allocate extra time and resources for handling unforeseen technical challenges.

2. Scope Creep:

Risk: There is a risk of expanding the project scope beyond its initial plan, leading to delays and budget overruns.

Mitigation: Define a clear project scope and stick to it. Any proposed scope changes should go through a formal change request process and be evaluated for their impact on timelines and costs.

3. Security Vulnerabilities:

Risk: Security vulnerabilities, such as API vulnerabilities or data breaches, can compromise the integrity of the pipeline.

Mitigation: Implement security best practices, conduct security audits, and stay updated on the latest security threats. Regularly test the pipeline for vulnerabilities and apply security patches promptly.

4. Resource Constraints:

Risk: Insufficient resources, including budget, time, or skilled personnel, can hinder project progress.

Mitigation: Continuously monitor resource allocation. If additional resources are required, request them early and justify their necessity. Consider resource scaling options on AWS for flexibility.

5. Alignment Challenges with SingularityNET:

Risk: Aligning the pipeline with SingularityNET may pose unforeseen challenges due to differences in architecture or communication protocols.

Mitigation: Collaborate closely with SingularityNET's technical team. Conduct thorough integration testing and ensure compatibility by following SingularityNET's guidelines and best practices.

6. Legal and Intellectual Property Issues:

Risk: Legal issues related to intellectual property, licensing, or data ownership could arise.

Mitigation: Conduct a thorough review of licenses for libraries and datasets used. Ensure compliance with relevant intellectual property laws and contracts. Seek legal advice when necessary.

Related Links

Include any relevant links, documents, or references that support and validate your proposal. This may include academic papers, previous projects, prototypes, or any other resources that showcase your capabilities.

Proposal Video

Phase 1 of Huggingface datasets library to SingularityNET Pipeline - #DeepFunding IdeaFest Round 3

26 September 2023
  • Total Milestones

    10

  • Total Budget

    $19,500 USD

  • Last Updated

    1 Mar 2024

Milestone 1 - R&D, Project / Framework Setup

Status
😀 Completed
Description

Research & DevelopmentSelect and set up the Python web framework.Configure the AWS environment, VCP instances, and necessary infrastructure.Initialize the version control system and create the project repository.

Deliverables

Budget

$1,500 USD

Milestone 2 - Modular Architecture Design and Data Retrieval

Status
😀 Completed
Description

Design the modular architecture of the pipeline.Develop the data retrieval component.Implement basic error handling and logging for data retrieval.

Deliverables

Budget

$2,000 USD

Milestone 3 - Data Transformation and Schema Mapping

Status
😀 Completed
Description

Build the data transformation module.Create schema mapping functions for dataset adaptation.Perform data preprocessing as needed.Implement data persistence and indexingIntegrate the Time Questions dataset from Huggingface.

Deliverables

Budget

$2,500 USD

Milestone 4 - Web API Development

Status
😀 Completed
Description

Develop an API for operating the system.Implement route handlers and controllers.Integrate data retrieval and transformation components into the API.Implement endpoint access management.

Deliverables

Budget

$2,000 USD

Milestone 5 - Security and Access Control

Status
😀 Completed
Description

Enhance security with custom Auth or other authentication methods.Implement access controls to restrict unauthorized access.Conduct security testing and vulnerability assessment.

Deliverables

Budget

$2,000 USD

Milestone 6 - Documentation and Testing

Status
😀 Completed
Description

Comprehensive API documentation.Develop unit tests and integration tests.Test various scenarios, including error handling.Set up a documentation website resource.

Deliverables

Budget

$2,500 USD

Milestone 7 - Cloud Deployment

Status
😀 Completed
Description

Configure AWS services, including ECS, RDS (if needed), and S3 for static assets.Set up load balancing and auto-scaling for scalability.Setup CI/CD pipelineDeploy the pipeline on AWS infrastructure.

Deliverables

Budget

$2,000 USD

Milestone 8 - SingularityNET Alignment

Status
🧐 In Progress
Description

Ensure compatibility with SingularityNET specifications.Establish communication channels or APIs for integration.Conduct integration testing with SingularityNET components.

Deliverables

Budget

$2,500 USD

Link URL

Milestone 9 - Performance Optimization and Monitoring

Status
😀 Completed
Description

Implement performance optimizations, such as asynchronous processing and Rate Limiting.Fine-tune AWS infrastructure for cost efficiency.Set up comprehensive monitoring and alerting.

Deliverables

Budget

$1,500 USD

Milestone 10 - Final Testing and Deployment

Status
🧐 In Progress
Description

Conduct a final round of testing, including load testing.Address any remaining issues or bugs.Deploy the final, production-ready pipeline.

Deliverables

Budget

$1,000 USD

Link URL

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