Project Overview:

This project has two key goals:

1. API Development and Deployment:

• Develop a robust and secure API to expose the AI model’s functionality for external customer use.
• Deploy the API as a scalable service and integrate it into the existing codebase to enable seamless interaction with customer-facing applications.

1. Data Pipeline Automation:

• Automate the process of fetching the latest data from your database, preprocessing it, and integrating it with existing training datasets.
• Schedule weekly model training to ensure it remains accurate and up-to-date.
• The pipeline must be fully automated and resilient, minimizing manual intervention.

Project Scope:

Part 1: API Development, Deployment, and Integration

1. Understanding the Model Requirements:

• Analyze the AI model's input/output structure and expected use cases.
• Identify the core functionalities that the API should expose to external users.

1. API Design and Development:

• Develop a RESTful API (or GraphQL, if preferred) to handle the following:
• Accept data inputs from users and send it to the model for processing.
• Return predictions or processed outputs to users in a standardized format.
• Implement error handling, input validations, and secure authentication (e.g., token-based).

1. API Deployment:

• Package the API using Docker for consistency across environments.
• Deploy the API on a scalable infrastructure such as AWS Lambda, Google Cloud Run, or Kubernetes.
• Ensure it is highly available and protected with appropriate security measures like OAuth2 or API Gateway.

1. Integration with Existing Codebase:

• Refactor the current codebase to interact with the newly created API endpoints.
• Add appropriate service calls and ensure smooth integration with frontend or customer-facing components.

Part 2: Automating the Data Pipeline with Database Integration

1. Fetching the Latest Data:

• Build a module to query the database for the latest records added since the last training cycle.
• Design a method to incrementally fetch only the new data (e.g., using timestamps or IDs).

1. Data Ingestion and Preprocessing:

• Extend the pipeline to:
• Merge the newly fetched data with existing datasets.
• Validate, clean, and preprocess the combined data for training.
• Use tools like Pandas, NumPy, or database ETL solutions for efficient data handling.

1. Automating Model Training:

• Schedule a weekly pipeline run using tools like Apache Airflow, Prefect, or AWS Step Functions.
• Integrate the pipeline with the AI model to retrain it using the updated dataset.

1. Deployment of the Updated Model:

• Replace the existing model with the newly trained one via an automated deployment step.
• Ensure downtime is minimized during model updates.

Additional Scope: Monitoring and Error Handling

1. Error Logging and Recovery:

• Implement robust error logging for failures in data fetching, preprocessing, or training steps.
• Set up retry mechanisms for transient errors (e.g., database connectivity issues).

1. Real-Time Alerts:

• Integrate alerting tools like Prometheus, CloudWatch, or Grafana to notify stakeholders of pipeline failures or degraded API performance.

Deliverables:

1. API Development and Deployment:

• Fully operational API deployed on a scalable infrastructure.
• Documentation for all API endpoints, including usage examples and error codes.

1. Database-Integrated Data Pipeline:

• Automated module to fetch and preprocess the latest data from your database.
• Weekly automated training pipeline integrated with the AI model.
• Trained model deployed with zero or minimal downtime.

1. Monitoring and Logs:

• A comprehensive monitoring dashboard to track API performance and pipeline health.
• Detailed logs for debugging and auditing purposes.

1. Documentation:

• Technical documentation for the API, data pipeline, and monitoring setup.
• A troubleshooting guide to address common issues.

Technologies and Tools:

1. API Development:

• Frameworks: FastAPI, Flask, or Express.js.
• Deployment: Docker, Kubernetes, AWS Lambda, Google Cloud Run.

1. Data Pipeline:

• Database Integration: SQLAlchemy, PostgreSQL client, or AWS RDS integrations.
• Scheduling and Automation: Apache Airflow, Prefect, AWS Step Functions.
• Preprocessing: Pandas, NumPy.

1. Monitoring and Error Handling:

• Monitoring: Prometheus, Grafana, CloudWatch.
• Logging: ELK Stack, AWS CloudTrail.

Expectations from Developers:

1. Collaborate to design the API and pipeline architecture.
2. Implement and test the API for usability and scalability.
3. Build an efficient data-fetching module that integrates with your database.
4. Ensure the pipeline runs reliably with minimal manual intervention.
5. Deliver clear documentation and provide knowledge transfer sessions.

Evaluation Metrics:

1. API Functionality:

• Usability and reliability of the API in production.
• Seamless integration with the existing codebase.

1. Pipeline Reliability:

• Weekly training success rate with no manual intervention.
• Accuracy and performance improvements in the trained model.

1. Monitoring:
2. 
   

• Availability and responsiveness of real-time alerts.