(Environment) (AWS, Mac) Project Execution and Modeling in an Enterprise Setting

I have resumed engineering and modeling tasks, which I had temporarily set aside to focus on project management and new business planning.

For a project that involves optimizing (tuning) individual models for image and text processing, as well as integrating multiple models—a skill I honed during my Ph.D. program—I now require a complex and well-structured work environment.

To ensure optimal efficiency under the given constraints, I plan to set up, refine, and document the work environment for future reference.


"If I had eight hours to chop down a tree, I would spend six hours sharpening the axe."

– Abraham Lincoln (1861–1865), the 16th President of the United States

  • Review of Work Environment

    1. Managing Work on an Unstable Server & Data Backup

    ✅ Backup & Record Management: Using SFTP to store and synchronize data in a structured manner.
    ✅ GitHub Usage: Limited to baseline model development and version control for key components.
    ✅ Code & Data Management:

    • Initially, using SFTP Sync to manage code and data across the team until the environment stabilizes.
    • Ensuring team members’ PCs are synced for redundancy and preventing data loss.

    2. Remote Access Setup

    ✅ Primary: ssh (Company Server, AWS)
    ✅ Secondary: ngrok (For remote access to Google Colab)

    🚧 Corporate Security Policy Issues:

    • Internal servers can connect to other internal servers, but external servers cannot access internal servers via SSH.
    • This likely stems from security policies restricting inbound SSH connections.
    • AWS is technically external, so SSH should work unless explicitly blocked for security, cost, or management reasons.

    3. Personal Work Environment

    ✅ Hardware: Apple Mac M1 Pro (2020)
    ✅ Development Tools:

    • VS Code Extensions: SFTP, SSH, Python, Jupyter Notebook, Beautify, JSON
    • Jupyter Notebook: Main local development environment
    • Google Colab: For cloud-based experiments

    4. Storage & Data Accessibility

    ✅ Company Internal Server

    • Access to image and text datasets for analysis
    • GPU-enabled servers available for model training

    ✅ Apple Mac M1 Pro as an SFTP Server

    • Acts as a sync server to store backups and mitigate GPU server instability
    • Each unit member’s PC will archive and synchronize important work

    ✅ Google Drive (Temporary Storage)

    • Large dataset & image storage
    • Collaboration with external teams for data exchange
    • Code storage & sharing, ensuring project continuity

    5. Version Control & History Management

    ✅ Git History installed on both the server and team members’ PCs to maintain commit logs and traceability.

    6. Collaboration & Communication

    ✅ Messaging Platform: Slack

    • Used for daily task tracking and logging issues within the unit.
    • Chosen over internal company messengers to facilitate future external collaboration.
    • Slack is integrated with GitHub, making it valuable for monitoring baseline models and code updates.
    • Team members must use company emails for Slack accounts.

    Summary & Next Steps

    🔹 Set up and validate SSH access to AWS (ensure company policy allows it).
    🔹 Refine SFTP sync strategy to improve data redundancy and workflow efficiency.
    🔹 Test Google Drive integration for seamless external collaboration.
    🔹 Monitor and adjust Slack usage as the team grows and external partners join.

    This setup balances security, flexibility, and efficiency, ensuring smooth project execution in a corporate environment with strict security policies and infrastructure limitations. 🚀

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