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I’m an AI & Machine Learning engineer with expertise in deve
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I’m an AI & Machine Learning engineer with expertise in deve
Cover image for Neuro Lend
My credit model scored
Neuro Lend My credit model scored 0.92 AUC. Then I changed one field on one applicant, female to male, and she went from approved to rejected. A lot of lending AI works like this: it says no in milliseconds, and nobody, not the customer and often not the bank, can say why. So I built NeuroLend. You fill in a loan application and watch the signal travel through every neuron until the output turns green or red. Then it shows why it decided, and the smallest change that would flip the answer. Rohan is rejected at 54% risk. If he cuts his other EMIs to ₹5,500 a month, or asks for ₹7.5 lakh instead of ₹9 lakh, he's approved. Here's what's happening underneath. It's a small 8 → 10 → 6 → 1 network trained on 40,000 synthetic Indian loan applications, where I deliberately gave gender zero effect on the outcome. The "why" uses Integrated Gradients against an average applicant instead of raw activations, because glowing neurons look great but explain nothing to a human. Counterfactuals come from a binary search on each input the applicant can actually change, and a fairness check re-scores every applicant with only gender flipped. That check caught the bias that accuracy missed. Retraining with gender cut out of the network dropped the effect to exactly zero, and AUC stayed at 0.92. It all runs in plain JavaScript in the browser, about 0.005 ms per decision, with no server. Code is in the comments. How does your team test lending models for bias today? Medium - https://medium.com/@anuragnagare77/neuro-lend-99cddd19faad GitHub - https://github.com/AnuragNagare/Neuro-Lend.git
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A Full Stack QA[6+ YOE] to perform End to End testing.
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A Full Stack QA[6+ YOE] to perform End to End testing.
Cover image for ⚠️ When Quality Assurance Is
⚠️ When Quality Assurance Is Ignored, The Cost Can Be Huge Software quality is not just about finding bugs — it is about protecting business, customers, and reputation. Here are some real-world examples where software failures created massive impact: 🔴 1. Knight Capital Group (2012) — $440 Million Loss A software deployment issue caused automated trading systems to behave incorrectly. Within minutes, the company faced enormous financial losses. Lesson: ✅ Strong release validation ✅ Automated regression testing ✅ Production-like testing environments are critical for high-risk systems. 🔴 2. Healthcare.gov (http://Healthcare.gov) Launch (2013) — Poor User Experience & Performance Issues The initial launch faced severe performance problems, slow response times, and users were unable to complete registrations. Lesson: ✅ Performance testing under realistic user load ✅ Scalability testing ✅ End-to-end validation should happen before production launch. 🔴 3. Amazon S3 Outage (2017) — Major Service Disruption A maintenance mistake impacted AWS services and caused outages for many businesses relying on cloud infrastructure. Lesson: ✅ Proper change validation ✅ Automated checks ✅ Fail-safe deployment processes are essential for reliable systems. 🔴 4. Toyota Recall Software Issues Software-related issues in automotive systems highlighted the importance of rigorous validation and safety testing. Lesson: When software controls critical systems, quality engineering becomes a business necessity. 💡 A single missed defect can impact: ❌ Revenue ❌ Customer trust ❌ Brand reputation ❌ Business continuity This is why QA is not a final checkpoint. Quality should be built into every stage of software development. #QualityEngineering #QA #TestAutomation #PerformanceTesting #SDET #SoftwareTesting #DevOps #AutomationTesting
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Software | Full-Stack Development | Quantum Computing
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Software | Full-Stack Development | Quantum Computing
InSite Tech Solutions: Websites, Software & More
InSite Tech Solutions: Websites, Software & More
Concept to Growth with Design, Development & Marketing
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Concept to Growth with Design, Development & Marketing