🚀 Architecture Plan for Open Cap Stack

[urbantech]

Overview

This architecture plan for Open Cap Stack integrates MLflow tracking, Delta Lake, CI/CD with GitHub Actions, and project management with Pivotal Tracker. It aims to create a robust, scalable, and efficient system for managing ML and data workflows, ensuring continuous integration and deployment, and maintaining a well-organized development process.

Components and Tools

• Data Storage and Processing: Delta Lake, Spark
• ML Lifecycle Management: MLflow
• API Management: Kong API Gateway
• CI/CD: GitHub Actions
• Version Control: GitHub
• Project Management: Pivotal Tracker

Architecture Plan

1. Data Storage and Processing

• Data Lake:
  • Use Delta Lake for storing raw and processed data.
  • Utilize Delta's support for various ecosystems (Iceberg, Hudi, Java, Rust, Python) to ensure compatibility with existing tools.
• Data Processing:
  • Use Apache Spark with Delta Lake for data processing and transformation tasks.

2. ML Lifecycle Management

• MLflow Tracking:
  • Start with a local setup for development and experimentation.
  • Move to a setup with various data stores (local file storage for artifacts, database for metadata) for intermediate scalability.
  • Finally, implement remote tracking with a tracking server, using cloud storage for artifacts and a database for metadata.
  • Ensure team access to the MLflow Tracking Server for collaboration.
• Model Training and Inference:
  • Use MLflow to manage model training, embedding, vector databases, and inference tasks.
  • Implement model store and feature store for efficient model and feature management.

3. API Management

• Kong API Gateway:
  • Use Kong for API management and developer portals.
  • Set up Kong to handle API requests, manage traffic, and ensure security.
  • Implement SaaS model for API gateway to ensure scalability and reliability.

4. CI/CD Pipeline

• GitHub Actions:
  • Implement CI/CD pipeline using GitHub Actions.
  • Set up workflows to automate testing, building, and deployment processes.
  • Ensure that each push to the main branch triggers automated tests and deployments.
  • Use feature branches for development and ensure pull requests are reviewed and tested before merging.
• GitHub:
  • Use GitHub for version control and storage of development code.
  • Maintain a well-structured repository with clear documentation and organized directories.
  • Use GitHub Issues for tracking bugs and feature requests.

5. Project Management

• Pivotal Tracker:
  • Use Pivotal Tracker to manage the backlog for software development.
  • Break down user stories into smaller tasks, score them based on complexity, and prioritize them accordingly.
  • Ensure regular updates and reviews of the backlog to reflect the current status and progress.
  • Use Pivotal Tracker to facilitate communication and collaboration among team members.

Implementation Plan

Step 1: Setup Version Control and Project Management

1. Create a GitHub repository for Open Cap Stack.
2. Set up Pivotal Tracker with user stories, tasks, and backlog.

Step 2: Configure Data Storage and Processing

1. Set up Delta Lake for data storage.
2. Configure Apache Spark with Delta Lake for data processing tasks.
3. Ensure data compatibility across various ecosystems (Iceberg, Hudi, Java, Rust, Python).

Step 3: Implement ML Lifecycle Management

1. Start with MLflow tracking on localhost for development.
2. Transition to a setup with local storage and databases for intermediate scalability.
3. Set up a remote tracking server with cloud storage and database for large-scale deployment.
4. Implement model store and feature store.

Step 4: Configure API Management

1. Set up Kong API Gateway for API management.
2. Configure Kong to handle API requests, manage traffic, and ensure security.
3. Implement the SaaS model for API gateway.

Step 5: Implement CI/CD Pipeline

1. Set up GitHub Actions for CI/CD.
2. Create workflows for automated testing, building, and deployment.
3. Ensure feature branches are reviewed and tested before merging.
4. Implement automated deployments to staging and production environments.

Step 6: Ongoing Project Management and Monitoring

1. Regularly update Pivotal Tracker with progress on user stories and tasks.
2. Conduct regular reviews and planning sessions to prioritize backlog items.
3. Monitor system performance and make necessary adjustments to ensure scalability and reliability.

Diagram Integration

1. Lakehouse Architecture Diagram:

   • Incorporate the Spark and Delta Lake components for data storage and processing.
   • Highlight the transition from raw data to processed data within the data lake.

2. MLflow Tracking Setup Diagram:

   • Integrate the three configurations (localhost, various data stores, remote tracking) into the overall architecture.
   • Show the progression from development to large-scale deployment.

3. Delta Lake Ecosystem Support Diagram:

   • Highlight the various ecosystems supported by Delta Lake.
   • Show how Delta Lake integrates with different tools and platforms within the architecture.

By following this detailed plan, we will establish a robust and scalable architecture for Open Cap Stack, integrating ML and data workflows, CI/CD, API management, and project management.