<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Abdul Majid - Engineering Notes &amp; Production Systems</title>
    <link>https://majidkumbhar.site/blogs/index.html</link>
    <description>Technical articles on AI Engineering, MLOps, Computer Vision, Quantitative Trading, and Resilient Python Architecture.</description>
    <language>en-us</language>
    <lastBuildDate>Mon, 14 Sep 2026 10:40:00 +0000</lastBuildDate>
    <atom:link href="https://majidkumbhar.site/rss.xml" rel="self" type="application/rss+xml" />

    <item>
      <title><![CDATA[What Actually Happens After model.fit()? The Real Anatomy of Production ML Systems]]></title>
      <link>https://majidkumbhar.site/blogs/what-happens-after-model-fit.html</link>
      <guid>https://majidkumbhar.site/blogs/what-happens-after-model-fit.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[A comprehensive deep dive into converting a trained Jupyter model into a scalable, monitored, and resilient microservice running in production with Docker, validation layers, and worker pools.]]></description>
      <category><![CDATA[AI Engineering & MLOps]]></category>
    </item>
    <item>
      <title><![CDATA[The ML Model Is Only 20% of the System: Engineering the Surrounding 80%]]></title>
      <link>https://majidkumbhar.site/blogs/ml-model-is-only-20-percent.html</link>
      <guid>https://majidkumbhar.site/blogs/ml-model-is-only-20-percent.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Why most AI systems fail outside the lab, and how to build the crucial surrounding 80%: caching layers, rate limiters, fallback heuristics, and circuit breakers.]]></description>
      <category><![CDATA[AI Engineering & MLOps]]></category>
    </item>
    <item>
      <title><![CDATA[Zero-Downtime FastAPI & Docker Deployments for PyTorch Models]]></title>
      <link>https://majidkumbhar.site/blogs/fastapi-docker-ml-deployment.html</link>
      <guid>https://majidkumbhar.site/blogs/fastapi-docker-ml-deployment.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Step-by-step guide to deploying deep learning inference containers with Nginx blue-green rolling updates, CPU core pinning, and zero dropped client packets.]]></description>
      <category><![CDATA[AI Engineering & MLOps]]></category>
    </item>
    <item>
      <title><![CDATA[The Anatomy of a Maintainable Python Machine Learning Project]]></title>
      <link>https://majidkumbhar.site/blogs/python-production-ml-project-structure.html</link>
      <guid>https://majidkumbhar.site/blogs/python-production-ml-project-structure.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How to structure an enterprise Python ML repository from scratch: separating data pipelines, model definitions, training loops, and serving layers.]]></description>
      <category><![CDATA[AI Engineering & MLOps]]></category>
    </item>
    <item>
      <title><![CDATA[Detecting Data Drift & Concept Drift in Production ML Systems]]></title>
      <link>https://majidkumbhar.site/blogs/monitoring-ml-models-in-production.html</link>
      <guid>https://majidkumbhar.site/blogs/monitoring-ml-models-in-production.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Practical mathematical methods to detect when real-world distributions diverge from training baselines using KS-tests, Population Stability Index (PSI), and lightweight logging.]]></description>
      <category><![CDATA[AI Engineering & MLOps]]></category>
    </item>
    <item>
      <title><![CDATA[Explainable Computer Vision: Implementing Grad-CAM Over Deep CNNs for Retinal Lesion Localization]]></title>
      <link>https://majidkumbhar.site/blogs/explainable-cv-efficientnet-gradcam.html</link>
      <guid>https://majidkumbhar.site/blogs/explainable-cv-efficientnet-gradcam.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[A clinical-grade engineering deep dive into implementing Gradient-weighted Class Activation Mapping (Grad-CAM) over EfficientNet architectures for fundus image interpretability.]]></description>
      <category><![CDATA[Computer Vision & XAI]]></category>
    </item>
    <item>
      <title><![CDATA[Building an Automated Image Quality & Artifact Validation Gate for Medical Fundus Scans]]></title>
      <link>https://majidkumbhar.site/blogs/image-quality-validation-pipeline.html</link>
      <guid>https://majidkumbhar.site/blogs/image-quality-validation-pipeline.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How to design a fast, heuristic and CNN-based pre-inference filter to reject blurred, overexposed, and low-contrast medical images before model evaluation.]]></description>
      <category><![CDATA[Computer Vision & XAI]]></category>
    </item>
    <item>
      <title><![CDATA[Understanding Grad-CAM Math: Saliency Gradients, Feature Maps, and Practical OpenCV Blending]]></title>
      <link>https://majidkumbhar.site/blogs/grad-cam-visual-explanation.html</link>
      <guid>https://majidkumbhar.site/blogs/grad-cam-visual-explanation.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[A mathematical walkthrough of class activation mappings, backpropagation through convolutional tensors, and producing beautiful diagnostic overlays.]]></description>
      <category><![CDATA[Computer Vision & XAI]]></category>
    </item>
    <item>
      <title><![CDATA[Why Softmax Probabilities Lie: Temperature Scaling & Calibrating Medical AI Models]]></title>
      <link>https://majidkumbhar.site/blogs/model-calibration-probabilities.html</link>
      <guid>https://majidkumbhar.site/blogs/model-calibration-probabilities.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Modern deep neural networks are notoriously overconfident. Discover how to apply Platt scaling and temperature scaling to align model confidence with real empirical probabilities.]]></description>
      <category><![CDATA[Computer Vision & XAI]]></category>
    </item>
    <item>
      <title><![CDATA[Lessons from Prototyping Clinical Decision Support Tools: Safety Guardrails & Human-in-the-Loop AI]]></title>
      <link>https://majidkumbhar.site/blogs/responsible-medical-ai-prototype-lessons.html</link>
      <guid>https://majidkumbhar.site/blogs/responsible-medical-ai-prototype-lessons.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Real-world engineering insights on building clinical decision support prototypes: audit logging, fail-safe UI design, and ethical clinician-in-the-loop workflows.]]></description>
      <category><![CDATA[Computer Vision & XAI]]></category>
    </item>
    <item>
      <title><![CDATA[Engineering 24/7 Python Daemons: Systemd, Dead-Man Switches, and Graceful Restarts]]></title>
      <link>https://majidkumbhar.site/blogs/reliable-24-7-python-automation.html</link>
      <guid>https://majidkumbhar.site/blogs/reliable-24-7-python-automation.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How to build bulletproof Python background worker processes that survive network partitions, server reboots, memory leaks, and OS kill signals.]]></description>
      <category><![CDATA[Python Engineering & Backend]]></category>
    </item>
    <item>
      <title><![CDATA[Transforming a Fragile Python Script into a Battle-Tested Background Microservice]]></title>
      <link>https://majidkumbhar.site/blogs/python-script-to-production-service.html</link>
      <guid>https://majidkumbhar.site/blogs/python-script-to-production-service.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[The step-by-step refactoring journey from a hardcoded, brittle script into an observable, configurable, and robust backend service.]]></description>
      <category><![CDATA[Python Engineering & Backend]]></category>
    </item>
    <item>
      <title><![CDATA[Asynchronous Workflows with FastAPI, Celery, and Redis: Handling Heavy Batch Computations]]></title>
      <link>https://majidkumbhar.site/blogs/fastapi-celery-redis-background-jobs.html</link>
      <guid>https://majidkumbhar.site/blogs/fastapi-celery-redis-background-jobs.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Decoupling web request-response lifecycles from long-running ML training, report generation, and data crunching tasks.]]></description>
      <category><![CDATA[Python Engineering & Backend]]></category>
    </item>
    <item>
      <title><![CDATA[Designing Resilient Web Scrapers & ETL Pipelines That Don't Crash on Cloudflare or Schema Changes]]></title>
      <link>https://majidkumbhar.site/blogs/designing-resilient-etl-pipelines.html</link>
      <guid>https://majidkumbhar.site/blogs/designing-resilient-etl-pipelines.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Production patterns for automated data extraction: exponential backoffs, fingerprint rotation, transactional staging tables, and dead-letter queues.]]></description>
      <category><![CDATA[Python Engineering & Backend]]></category>
    </item>
    <item>
      <title><![CDATA[Playwright vs Selenium for Large-Scale Data Extraction: Concurrency, Fingerprinting, and Memory Leaks]]></title>
      <link>https://majidkumbhar.site/blogs/playwright-vs-selenium-production-scraping.html</link>
      <guid>https://majidkumbhar.site/blogs/playwright-vs-selenium-production-scraping.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[An empirical comparison of Playwright and Selenium in headless server environments: measuring RAM usage, browser context isolation, and anti-bot stealth.]]></description>
      <category><![CDATA[Python Engineering & Backend]]></category>
    </item>
    <item>
      <title><![CDATA[What I Learned Building a Vectorized Python Backtesting Suite from Scratch]]></title>
      <link>https://majidkumbhar.site/blogs/building-python-backtesting-engine-lessons.html</link>
      <guid>https://majidkumbhar.site/blogs/building-python-backtesting-engine-lessons.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Architecture and mathematical lessons from engineering a custom quantitative backtesting engine modeling slippage, commission tiers, and execution latency.]]></description>
      <category><![CDATA[Quant & Algorithmic Trading]]></category>
    </item>
    <item>
      <title><![CDATA[5 Deadly Backtesting Pitfalls That Inflate Simulated Sharpe Ratios & Wipe Out Capital]]></title>
      <link>https://majidkumbhar.site/blogs/backtesting-pitfalls-inflated-performance.html</link>
      <guid>https://majidkumbhar.site/blogs/backtesting-pitfalls-inflated-performance.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[An autopsy of common backtesting mistakes: survivorship bias, data snooping, look-ahead bias, and regime overfitting.]]></description>
      <category><![CDATA[Quant & Algorithmic Trading]]></category>
    </item>
    <item>
      <title><![CDATA[Converting Quantitative Rules into 24/5 MetaTrader 5 (MT5) Execution Daemons]]></title>
      <link>https://majidkumbhar.site/blogs/rules-to-mt5-bot-automation.html</link>
      <guid>https://majidkumbhar.site/blogs/rules-to-mt5-bot-automation.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How to build a stable Python-to-MetaTrader 5 automation bridge handling socket reconnects, order execution slips, and margin validation.]]></description>
      <category><![CDATA[Quant & Algorithmic Trading]]></category>
    </item>
    <item>
      <title><![CDATA[Why Win Rate Is a Trap: Mathematical Expectancy, Payoff Ratios, and Asymmetric Returns]]></title>
      <link>https://majidkumbhar.site/blogs/why-win-rate-is-a-trap.html</link>
      <guid>https://majidkumbhar.site/blogs/why-win-rate-is-a-trap.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[A quantitative breakdown of why a 90% win rate strategy can bankrupt an account, while a 35% win rate trend-following system compounds wealth.]]></description>
      <category><![CDATA[Quant & Algorithmic Trading]]></category>
    </item>
    <item>
      <title><![CDATA[Position Sizing & Maximum Drawdown Control: Kelly Criterion vs Fixed Fractional in Live Markets]]></title>
      <link>https://majidkumbhar.site/blogs/drawdown-risk-management-automated-trading.html</link>
      <guid>https://majidkumbhar.site/blogs/drawdown-risk-management-automated-trading.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How to mathematically size trading allocations to maximize geometric growth while avoiding the ruinous drawdowns of full Kelly betting.]]></description>
      <category><![CDATA[Quant & Algorithmic Trading]]></category>
    </item>
    <item>
      <title><![CDATA[Architecting a Multi-Service Super-App for Emerging Tier-2 Cities: The Sahulat Experience]]></title>
      <link>https://majidkumbhar.site/blogs/architecting-multi-service-superapp-local-city.html</link>
      <guid>https://majidkumbhar.site/blogs/architecting-multi-service-superapp-local-city.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[System architecture lessons from developing Sahulat: unifying rides, food delivery, grocery, and home services into a single resilient platform.]]></description>
      <category><![CDATA[Real-World Product Architecture]]></category>
    </item>
    <item>
      <title><![CDATA[Geolocation & Dispatch Logic for Local Ride-Hailing Without Multi-Million Dollar Infrastructure]]></title>
      <link>https://majidkumbhar.site/blogs/ride-dispatch-architecture-without-uber-scale.html</link>
      <guid>https://majidkumbhar.site/blogs/ride-dispatch-architecture-without-uber-scale.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How to build low-latency geospatial rider-driver matching using Redis Geohashes, PostGIS, and Hungarian optimization algorithms on modest cloud budgets.]]></description>
      <category><![CDATA[Real-World Product Architecture]]></category>
    </item>
    <item>
      <title><![CDATA[Engineering Software for Cash-First Economies: Ledger Reconciliation, OTP Fallbacks, and Low-Bandwidth Networks]]></title>
      <link>https://majidkumbhar.site/blogs/engineering-software-cash-first-markets.html</link>
      <guid>https://majidkumbhar.site/blogs/engineering-software-cash-first-markets.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Defensive design patterns for operating e-commerce and on-demand delivery systems where digital cards are rare and physical cash flows dominate.]]></description>
      <category><![CDATA[Real-World Product Architecture]]></category>
    </item>
    <item>
      <title><![CDATA[Managing Multi-Tenant Micro-SaaS Deployments with Docker, Nginx Virtual Hosts, and Automated SSL]]></title>
      <link>https://majidkumbhar.site/blogs/multi-tenant-agency-architecture-docker.html</link>
      <guid>https://majidkumbhar.site/blogs/multi-tenant-agency-architecture-docker.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[How we orchestrate and host multiple client platforms, subdomains, and staging environments on isolated Docker networks with automated Let's Encrypt certificates.]]></description>
      <category><![CDATA[Real-World Product Architecture]]></category>
    </item>
    <item>
      <title><![CDATA[Digitizing Offline Brick-and-Mortar Operations: Practical Lessons from Restaurant & Retail Portals]]></title>
      <link>https://majidkumbhar.site/blogs/from-local-workflow-to-digital-product.html</link>
      <guid>https://majidkumbhar.site/blogs/from-local-workflow-to-digital-product.html</guid>
      <pubDate>Mon, 14 Sep 2026 10:40:00 +0000</pubDate>
      <description><![CDATA[Case study on transforming manual pen-and-paper ordering, kitchen queues, and inventory workflows into intuitive digital software: The Al Shehryar platform.]]></description>
      <category><![CDATA[Real-World Product Architecture]]></category>
    </item>
  </channel>
</rss>
