What are the key milestones you need to achieve in order to complete your project successfully?
Milestone 1: <u>Data Source Identification and Collection Setup</u>
- Identify and finalize data sources for land registry information.
- Set up mechanisms to collect data from identified sources.
- Ensure data collection processes are automated and reliable.
- Find proper training data that will be relevant, like land rights record in different countries we will initially target. (US, Germany, Netherlands, India)
- Research, download and store the relevant training data
Acceptance criteria:
- Identified data sources for land registry information and established mechanisms for data collection.
- Data collection processes automated and running smoothly.
Dependencies:
- Access to Data Sources: Availability of access to land registry data sources is required for data collection.
- Database Infrastructure: Setup and configuration of the database infrastructure are necessary for data storage.
Risks and Mitigation:
- Data Source Availability: Ensure backup data sources are available in case of unavailability or issues with primary data sources.
- Data Integrity: Implement data validation checks and error handling mechanisms to ensure data integrity during collection and storage processes.
Timeline:
- Duration: 12 weeks
- Start Date: 01-12-2024
- End Date: 28-02-2025
Milestone costs
Milestone 2: Overview:
This milestone focuses on implementing backend infrastructure and services to support the deployment and execution of AI models. It involves setting up the necessary backend architecture, integrating AI models, and developing APIs for model inference and data processing.
Backend Architecture Setup
- Design and set up backend infrastructure to support AI model deployment.
- Select appropriate technologies and frameworks for backend development.
- Ensure scalability, reliability, and security of the backend architecture.
AI Model Integration
- Integrate AI models into the backend infrastructure.
- Develop mechanisms for model loading, initialization, and execution.
- Ensure compatibility and interoperability with different types of AI models.
API Development for Model Inference
- Design and implement APIs for model inference and prediction.
- Define input and output formats for API endpoints.
- Implement error handling and validation mechanisms for API requests.
Acceptance Criteria:
Backend Architecture Setup:
- Backend infrastructure set up and configured according to requirements.
- Technologies and frameworks selected for backend development are suitable for the project needs.
AI Model Integration:
- AI models successfully integrated into the backend infrastructure.
- Mechanisms for model loading, initialization, and execution implemented and tested.
API Development for Model Inference:
- APIs for model inference and prediction developed and documented.
- APIs demonstrate proper input validation and error handling.
<u>Timeline:</u>
Duration: 7 months
Start Date: 01-03-2025
End Date: 30-08-2025
Milestone execution cost
250K
Milestone 3: Overview:
This milestone focuses on testing and integrating AI models into the system. It ensures that the models perform accurately and efficiently within the application environment
Acceptance Criteria
Test Environment Setup
- Set up a dedicated test environment for AI model testing.
- Install necessary tools and frameworks for testing.
- Define testing protocols and standards.
Unit Testing
- Develop and execute unit tests for individual components of the AI models.
- Verify the functionality and behavior of each component.
- Ensure that unit tests cover all critical aspects of the models.
Integration Testing
- Integrate AI models with the application or platform.
- Conduct integration tests to ensure seamless interaction between models and other system components.
- Verify data flow and communication channels between the models and the rest of the system.
Performance Testing
- Evaluate the performance of AI models under various conditions (e.g., different input data volumes, concurrent requests).
- Measure response times, throughput, and resource utilization.
- Identify and address performance bottlenecks.
User Acceptance Testing (UAT)
- Invite end users or stakeholders to participate in User Acceptance Testing (UAT).
- Validate the functionality and usability of AI models from the user's perspective.
- Gather feedback and address any issues or concerns raised during UAT.
Timeline:
- Duration: 8 weeks
- Start Date: 01-09-2025
- End Date: 01-12-2025
Milestone execution cost
40K
Final Milestone: Overview:
This milestone focuses on documenting the AI model to ensure clarity, transparency, and ease of understanding for stakeholders, developers, and users. Documentation plays a crucial role in explaining the model's functionality, architecture, and usage.
Model Architecture and Functionality Documentation
- Document the architecture of the AI model, including its components, layers, and flow of data.
- Describe the functionality of each component and how they contribute to the overall model's behavior.
- Include diagrams, charts, and illustrations to visualize the model's structure and operations.
Usage and Integration Documentation
- Provide guidelines and instructions for using the AI model, including input data formats, parameters, and output interpretation.
- Document integration steps for developers to integrate the model into applications or systems.
- Include code examples, API documentation, and troubleshooting tips for seamless integration.
Timeline:
- Duration: 3 months
- Start Date: 01-12-2025
- End Date: 02-03-2026
Milestone execution cost
50K
