🚀 Imagining an OpenAI-like Company in India: Building the Future of Artificial Intelligence
Bikram Sarkar
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28 posts published so far
🛰️ The LLM Observability Stack: What to Track and Why
Bikram Sarkar
🪦 Prompt Engineering Is Dead. Long Live Prompt Architectures
Bikram Sarkar
🧲 How to Build a Vector Database That Doesn’t Suck
Bikram Sarkar
🤖 RAG vs Fine-Tuning: Which One Is Right for You?
Bikram Sarkar
🏪 Feature Stores: Do You Actually Need One?
Bikram Sarkar
📊 10 Metrics You’re Not Logging — But Absolutely Should
Bikram Sarkar
🧪 ML Experiments at Scale: The Hidden Ops Cost No One Talks About
Bikram Sarkar
📄 How to Write an ML Design Doc
Bikram Sarkar
🧠 Postmortems for ML Models: How to Run One Without Blame
Bikram Sarkar
📉 Data Drift Early Warning Systems: DIY vs SaaS
Bikram Sarkar
🤖 How to Automate ML Evaluation Without Going Full Kubeflow
Bikram Sarkar
🧠 The MLOps Tool Fatigue Problem: Too Many Tools, Too Little ROI
Bikram Sarkar
🧰 Building a 1-Person MLOps Stack That Works
Bikram Sarkar
🐞 Debugging ML Systems: From Data to Deployment
Bikram Sarkar
🔁 Retraining Strategies: Time-Based vs Event-Based
Bikram Sarkar
📊 Monitoring Your Deployed Models: Metrics That Matter
Bikram Sarkar
🚀 Deploy ML Models Using TensorFlow & AWS SageMaker
Bikram Sarkar
🔁 CI/CD for Machine Learning: A Step-by-Step Guide
Bikram Sarkar
🛠️ Design, Develop & Maintain Scalable End-to-End ML Pipelines
Bikram Sarkar
🧱 The Myth of ‘Just Add More Data’ in ML
Bikram Sarkar
💸 The Hidden Costs of Training at Scale
Bikram Sarkar
🌐 Serving ML Models as APIs: Flask vs FastAPI vs LangChain
Bikram Sarkar
🤖 AutoML vs Manual ML: What You Gain, What You Lose
Bikram Sarkar
📦 ML in Production: Data Drift, Retraining & Monitoring
Bikram Sarkar