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AI Comparison Tool / EdPlus AI model

AI Comparison Website

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Business problem we are solving for

The business problem we are solving for is the need for a comprehensive AI comparison tool that allows users to evaluate and compare multiple AI models based on quality, cost, and other notable differences. This tool will help individuals and organizations make informed decisions when selecting AI models for various tasks and applications.

Summary of project

The project involves developing an AI comparison tool that will provide a user-friendly interface for testing and comparing multiple AI models using the same prompt. The tool will enable users to assess the following qualities associated with each model, empowering them to make informed decisions based on their specific requirements.

Accuracy and Precision: Assess the models' ability to provide accurate and precise outputs based on the given prompts. Look for variations in their performance, as some models may excel in certain domains or tasks while others may struggle.

Interpretability and Explainability: Evaluate how well the AI models can explain their decision-making process and provide interpretability. Some models may offer more transparent explanations, making it easier for users to understand and trust the results.

Training Data Requirements: Analyze the data requirements of each model. Some models may require large amounts of training data, while others may be more data-efficient. This difference can impact the time and resources needed to train and deploy the models.

Model Size and Complexity: Consider the size and complexity of the models. Larger models may offer better performance but require more computational resources. The trade-off between model size and performance is an important factor to consider, especially in resource-constrained environments.

Training and Inference Speed: Compare the training and inference speeds of the models. Faster models can be advantageous for real-time or time-sensitive applications, while slower models may provide higher accuracy at the cost of speed.

Support and Community: Evaluate the level of support and the size of the user community associated with each AI model. Robust support and an active community can be valuable for troubleshooting issues, accessing documentation, and sharing knowledge.

Pretrained Models and Transfer Learning: Look for differences in the availability and quality of pretrained models for various tasks. Some models may offer better pretrained models, enabling transfer learning and faster adaptation to specific domains.

Ethical Considerations: Assess the ethical implications associated with each model. Look for potential biases, fairness issues, or other ethical concerns that may arise from using the models in different contexts.

Integration and Deployment Complexity: Consider the ease of integrating and deploying each model within your specific infrastructure or application. Differences in deployment requirements, dependencies, or compatibility can impact the implementation process.

Long-term Support and Updates: Investigate the track record of each AI model in terms of long-term support, maintenance, and updates. Models with a history of frequent updates and improvements may indicate a more active and reliable development team.

Goals

Enable Comprehensive AI Model Comparison: Provide a platform that enables users to compare multiple AI models based on factors such as quality, cost, accuracy, interpretability, speed, and other relevant metrics.

Facilitate Informed AI Decision-Making: Empower users to make informed decisions when selecting AI models by providing them with a comprehensive assessment of the models' strengths, weaknesses, and trade-offs across different dimensions.

Expedite AI Testing and Research: Streamline the process of testing and evaluating AI models by providing a centralized platform that allows users to easily and efficiently compare multiple models. This goal aims to save time and resources in the testing and research phases of AI model selection.

Increase Awareness of Available AI Resources: Increase awareness and knowledge about the wide range of AI models and resources available by showcasing different models within the comparison tool. This will help users discover and explore AI models they may not have been previously aware of, thereby expanding their options and possibilities.

Primary Features:

A tool for comparing different AI models The tools allows us to test multiple AI models at the same time to compare quality, cost and customization. ASUO Fined Tuned AI Model Using the content provided by the coach, the co pilot is capable of providing a short script for the coach to follow or modify that answers the students question and keeps the conversation flowing. AI Feedback and testing Platform Depending on the questions and resources being searched for within the copilot, we can recommend additional questions that may be helpful to a given conversation.

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Scope of project (TBD)

The project will include the following stages:

Resources/teams involved

Project Manager: Responsible for overall project coordination, communication, and timeline adherence.

Product Owner: A project stakeholder who will act as the voice of the user and make decisions about the product direction.

AI/ML Engineers: To build, train, and test the AI models.

Data Engineers/Analysts: To handle data aggregation, cleaning, and preprocessing.

Software Developers: For system integration, API management, and front-end interaction.

QA & UX Research: To conduct rigorous testing on the results of the AI models

AI Comparison Tool 2

AI Comparison Tool 2

Name

Description

Project step

Start Date

Duration

Status

Dependent

Project

Add Google Vertex AI model

Model Development

7/1/2023

14 days

Not started