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    How To Use ChatGPT Link Reader Plugin To Read Web Content (Easy Guide)

    Whether you are an avid reader, or simply someone who wants to finish out a lot of documents in a short amount of time, Link Reader can be a great option for you. It’s a ChatGPT plugin that can read, summarize, and analyze lengthy texts, PDFs, web pages, research papers, etc. So, if you are…

  • How To Use ChatGPT Instacart Plugin For Recipe And Ingredient Recommendations (Simple Guide)

    Instacart, the famous grocery delivery service has recently launched their Instacart plugin for ChatGPT that allows the users to order groceries according to the recipe. It can also recommend ingredients according to the recipe as well as your preferred or desired meal plan. So, whether you are planning to cook a meal for your family…

  • Why Your ML Pipeline Is Breaking in Production And How to Fix It

    Machine learning prototypes like a dream and deploys like a nightmare If we ask any team that’s scaled an ML project beyond a notebook, and they’ll tell you: getting a model to work is the easy part. Keeping it working—correctly, reliably, and ethically—in production? That’s where the real battle begins. Let’s talk about the cracks that appear when ML hits the real world, and what seasoned teams do to patch them before they widen. The Most Common Failure Points in Production ML 1. Data Drift: Your Model Is Learning from Yesterday’s World You trained your model on data from Q2. It’s now Q4, and user behavior has shifted, supply chains have rerouted, or the fraud patterns have evolved. Meanwhile, your model is confidently making predictions based on a world that no longer exists. How to Fix It: 2. Silent Failures: No One Knows It’s Broken Until It’s Too Late Your model outputs are being used downstream in production systems. The problem? It’s spitting out garbage—but it’s well-formatted, looks fine, and no one’s checking. How to Fix It: 3. Feature Leakage & Inconsistency: Your Training and Production Logic Don’t Match In training, you cleaned, transformed, and imputed data in a controlled environment. In production, the feature pipeline was reimplemented (or worse, manually replicated), and now your model is operating on a different reality. How to Fix It: 4. Retraining Without a Strategy: You’re Flying Blind You retrain your model weekly. Cool. Why? Is it helping? Are you tracking whether performance is improving—or quietly regressing? How to Fix It: 5. Lack of Observability: You’re Operating Without a Dashboard No logs. No metrics. No dashboards. If something goes wrong, it’s a post-mortem and a prayer. Without visibility, you’re not in control—you’re guessing. How to Fix It: 6. Ownership Gaps: Who Owns the Model After Launch? The data scientist shipped the model. The ML engineer deployed it. The product manager doesn’t know if it’s still performing. Sound familiar? How to Fix It: ✅ The Real Fix ML in production isn’t a project—it’s a system. And like any living system, it needs care, monitoring, and adaptation. What the best teams do: Closing Remarks Most ML failures in production aren’t algorithmic—they’re operational. The tech isn’t broken. The system around it is. If you’re serious about ML, stop treating models as one-off experiments. Start thinking like a systems engineer, not just a data scientist. Because in production, the model is only 10% of the problem—and 90% of the responsibility. Table Of Contents The Most Common Failure Points in Production ML ✅ The Real Fix Closing Remarks Subscribe to our newsletter & plug into the world of technology…

  • You’ve Reached The Current Usage Cap For GPT-4 | How to Fix

    Since the groundbreaking launch of ChatGPT, generative AI (Artificial Intelligence) has been at the center of attention for all of us. The chatbot uses vast amounts of scraped data to generate responses to human input that are accurate most of the time. Previously based on GPT-3.5, it has now been upgraded to GPT-4 and is…

  • How to Resolve ChatGPT 404 Not Found Errors (3 Easy Methods)

    While using ChatGPT, users around the world have faced numerous errors like 403, 404, 524, 1020, and so on. Among them, one of the most common errors is 404 not found errors. Basically, this error shows up when you are trying to navigate to ChatGPT.  The 404 is an HTTP error code and it happens…

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