Dipanshu Haldar

Applied Machine Learning: Building and deploying scalable, production-grade ML systems MLOps: Model lifecycle management, monitoring, CI/CD, and reproducibility Generative AI & LLMs: RAG systems, LLM evaluation, fine-tuning, and prompt optimization Data-Centric AI: Improving data quality, labeling strategies, and weak supervision Causal & Decision Intelligence: Experimentation, uplift modeling, and causal inference Efficient ML: Model optimization, distillation, and cost–performance tradeoffs Responsible AI: Fairness, explainability, and safe AI practices Domain-Aware ML: Translating business problems into high-impact ML solutions