July 30, 2025

Quick Insights to Start Your Week

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Welcome to this week’s CICD/DevOps huddle – your go-to source for the latest trends, industry insights, and tools shaping the industry. Let’s dive in! 🔥

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How to Clean Up Resources in Azure

Azure resource cleanup is a critical practice for maintaining cost efficiency and organizational clarity. The process involves removing unused or unnecessary resources—like apps, storage, or virtual machines—to prevent unexpected charges and streamline your environment. As noted in the original article, failure to clean up can lead to significant financial risks.

Understanding Azure Clean Up

In Azure, “clean up” refers to the deliberate removal of resources that are no longer needed. This includes virtual machines, storage accounts, and other services. By organizing your resources and deleting idle items, you not only save costs but also simplify management. The Azure portal provides a straightforward way to identify and delete these resources, starting with the resource group, which acts as a container for related services.

Key Steps for Resource Cleanup

  • Remove Delete Locks: Before deleting a resource, ensure any delete locks are cleared. Locks prevent accidental deletions, but they must be removed manually.
    • Navigate to the resource in the Azure portal.
    • Go to Locks under Settings and remove the lock before proceeding.
  • Delete the Resource Group: Resource groups allow bulk deletion of associated resources.
    • Search for “Resource groups” in the Azure portal.
    • Select the group and confirm deletion. Note that this process can take up to 5 minutes.
  • Handle NetworkWatcherRG: If this resource group was created during the guided project, delete it separately using the same process. Avoid deleting it if it existed before the project started.

Final Notes

The cleanup process ensures your Azure environment remains tidy and cost-effective. For detailed instructions, refer to the original guide:

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From Development to Deployment: Automating Machine Learning

Automating machine learning (ML) deployment is a game-changer for data scientists and MLOps engineers. This article walks through a workflow that transforms model training into a reusable, CI/CD-friendly process using Docker, Terraform, and Python scripts. The goal? Save hours of repetitive work while ensuring consistency across environments.

Key Tools & Workflow

The project relies on these essential tools:

  • scikit-learn for model training
  • FastAPI to serve the model as an API
  • Docker to containerize the model and infrastructure
  • Terraform for infrastructure-as-code (IaC)
  • Python scripts to automate repetitive tasks

The process is split into three main phases:

  1. Model Development & API Setup
    • Trained a logistic regression model on the Iris Species dataset.
    • Serialized the model using pickle into model.pkl.
    • Built a FastAPI server in app.py to handle /predict endpoints.
  2. Automation Scripts
    • Two Python scripts automate model training, Docker image building, and Terraform execution.
    • These scripts eliminate manual steps like updating config files or rebuilding Docker images.
  3. Cloud Deployment with Terraform
    • Terraform configuration files (main.tf, variables.tf, etc.) define cloud resources like Azure Resource Groups and Container instances.
    • The terraform Docker image runs commands (init, plan, apply) without requiring local installations.

Why This Matters

This approach minimizes dependencies, ensures environment consistency, and integrates DevOps best practices. By encapsulating the model and infrastructure in Docker, teams can scale reliably while reducing human error. The GitHub repo here provides all scripts and configs for replication.

Final Takeaway: Automation isn’t just about efficiency—it’s about empowering teams to focus on innovation. 🚀

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Fixing Engineering’s Biggest Time Suck: Finding Information

Key Takeaway:
Engineering teams waste more time finding information than dealing with tech debt, testing, or code reviews—a shocking twist from Atlassian’s “State of Developer Experience 2025” report. 🚨

The Stats That Shock:

  • 50%+ developers lose >10 hours/week to organizational inefficiencies.
  • 90% waste ≥6 hours/week on info hunts.
  • While 68% claim AI saves 10+ hours/week, the gains are offset by friction outside code editors.

Why AI Alone Won’t Fix It:
The report reveals a paradox: developers are overloaded with tools, yet still stuck in a “hot potato” game of ownership and orphaned knowledge. 🌀

The Real Culprits:

  1. Fragmented workflows and siloed knowledge.
  2. Cognitive load from new tech and context-switching.
  3. Poorly maintained IDPs (Internal Developer Portals) that fail to scale.

The Solution?
Enter Aviator Teams, a central hub blending AI’s intelligence with traditional IDP benefits. The goal? Turn info-finding into a breeze so devs can focus on building, reviewing, and shipping. 🚀

Bottom Line:
Stop chasing AI shortcuts. The real fix? Organizational redesign—and a dash of AI smarts. 💡

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🛠️ Tool of the Week

TeamCity, a build management and continuous integration server developed by JetBrains, was initially released on October 2, 2006. It operates as commercial software and is licensed under a proprietary license. This license offers a freemium model, providing up to 100 build configurations and three free Build Agent licenses..

🤯 Fun Fact of the Week

Currently, AWS, Azure, and GCP are the most widely adopted cloud platforms globally (source: Flexera). Amazon Web Services (AWS) has consistently been the preferred cloud service provider (CSP) for the majority of users since 2006, maintaining its dominant market share (33%) compared to Azure’s 23% and GCP’s 11%. However, Microsoft’s Azure Cloud has made significant strides over the years, surpassing AWS in certain domains, as reported by Flexera.

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