polmoz/december2023-study

• End to End Data Lab Basics

  • Research and study the basics of data labs, focusing on end-to-end processes.
  • Utilize YouTube tutorials on data pipelines, specifically on deploying to Azure.
  • Practice building a simple data pipeline.

  DATA PIPELINE PROJECT REFERENCES:

  1. [Azure Data Factory project ] (https://www.youtube.com/watch?v=IaA9YNlg5hM&t=303s)

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• **Home Lab ADD-ONs **

  • Set up a virtual desktop or deploy a dummy tech stack at home to simulate an on-premises environment. (Kasm Workspaces)
  • Work on setting up and configuring ADB (Azure Database?).
  • Explore Kubernetes (K8s) as a container orchestration tool.

  HOME LAB REFERENCES:

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• AI/ML Labs/Training

  • Engage in AI/ML training through platforms like O'Reilly.
  • Get hands-on experience with AI/ML tools and techniques.
  • Practice with Jupyter Notebooks for interactive computing and development.

  AI, ML REFERENCES:

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• Automation

  • Study and implement network automation (potentially using VPN).
  • Explore R&D on modern automation tools and practices.
  • Create a home automation assistant project.
  • Look into WAN optimization techniques.
  • Explore n8n
  • Explore node red

  AUTOMATION REFERENCES:

  1. [n8n Docs] (https://docs.n8n.io/)
  2. [node-red] (https://nodered.org/)

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• Observability

  • Learn about observability in systems and networks.
  • Implement monitoring tools like Telegraf in a lab environment.

  OBSERVABILITY REFERENCES:

  1. [Network Monitoring Solution - Telegraf] (https://www.linkedin.com/pulse/network-monitoring-solutions-demo-simple-robust-vijay-kumar-devunuri/)

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• Development/DevOps

  • Dive into DevOps principles and practices.
  • Work on a project that incorporates CI/CD pipelines.

  DEVOPS REFERENCES:

  1. [Model-Driven DevOps] (https://learning.oreilly.com/library/view/model-driven-devops-increasing/9780137644711/)

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