Ramana Varma

With the rapid rise of GenAI and Agentic AI trainings, it can be challenging to identify programs that truly balance theory with real-world application. Recently, my Data Science team at Target In India and I attended an abridged two-day Agentic AI training led by Abhijith Neerkaje and Ajay Shenoy —both former colleagues whose deep retail and data science expertise significantly elevated the experience. Drawing from their hands-on leadership of retail data science teams, they clearly explained core concepts while grounding them in practical use cases such as supply chain and pricing optimization. The sessions were engaging, practical, and rich with insights directly applicable to live projects. Their clarity, patience, and focus on real-world relevance stood out. I highly recommend their full Agentic and Generative AI programs to anyone looking to effectively bridge theory and industry application.

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People often ask me: “What does it actually take to grow into Product Data Science?” It’s one of the most exciting, fast-growing roles today. You sit at the intersection of product thinking, experimentation, causal inference, and applied AI, and you directly influence how product decisions get made. The truth is that A/B testing remains the foundation. And the complexity of running trustworthy experiments is only increasing. If you’re passionate about this, it’s a great moment to invest in your skills. Both of the A/B Testing courses below are currently 25% off: 1) A/B Testing 101 For PMs and Data Scientists beginning their experimentation journey • Fundamentals of A/B tests and how they drive product decisions • Writing hypotheses tied to business outcomes • Metric foundations, experiment setup and interpretation Prereqs: curiosity and a product mindset 🔗 https://lnkd.in/e4znBEqu 2) Advanced A/B Testing For Data Scientists who want to go deeper into experimental rigor • Handling pitfalls like SRM, peeking and noisy metrics • Sequential testing, variance reduction and Bayesian approaches • Making real-world ship or no-ship decisions 🔗 https://lnkd.in/eNHpn7mt These courses are intentionally designed to be sequential. → 101 builds the fundamentals. → Advanced takes you into high-rigor experimentation and decision science. 🎉 Both are 25% off this week. Don’t miss it.

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Ever feel like today’s AI models are getting smarter but somehow less useful? You’re not imagining it. LLM benchmarks have become the obsession. Model makers are locked in a cycle of chasing leaderboard points on MMLU, GSM8K, and other tests no one actually cares about outside of research labs. The result? Models that ace exams but fall apart in real-world use. They misinterpret vague instructions. They forget what you said three messages ago. They can’t think beyond a single step. But sure, the score went up by 0.3 percent. Benchmarks were supposed to be a guide. Instead, they’ve become a crutch. A way to show “progress” without improving anything that matters. The questions we should be asking are simple: Can the model follow messy, human instructions? Can it reason through problems, not just regurgitate answers? Can it actually help people get real work done? If you’ve worked with these models, you’ve felt the gap. You’ve seen the hype, and then you’ve seen it fall short when it counts. Are we building for people, or are we building for points? I’d love to hear what others are seeing in the wild. #AI #LLMs #Benchmarks #MachineLearning #RealWorldAI

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🔍 Actuarial Science Meets AI: Revolutionizing Non-Life Insurance Portfolio Monitoring 🚀 The integration of AI into actuarial science is transforming non-life insurance, particularly in portfolio monitoring. By leveraging machine learning, insurers can enhance risk assessment and predictive modeling with unprecedented precision. 📊 AI analyzes vast datasets—from claims history to real-time external factors—to detect emerging risks, optimize pricing, and improve reserve accuracy. For instance, AI-driven models can predict claim frequency and severity more dynamically, adapting to trends like climate change or economic shifts. This enables proactive portfolio adjustments, reducing volatility and boosting profitability. At the heart of this digital transformation? Trusted partners like RiskIQ, who empower insurers with cutting-edge tools to navigate this evolution seamlessly. The future of actuarial work is data-driven, agile, and AI-powered. Are you ready? 🤖 #ActuarialScience #AI #InsuranceTech #RiskAssessment #PredictiveModeling #DigitalTransformation #RiskIQ

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Building an LLM - concepts and interview question series Topic - Self-Attention If you have seen my profile header you know the Vaswani et al paper is one of my favorites but often in interviews when asked to give an intuition about "How transformers work?" - the candidates jump straight to the equation (formula 1 in the pic). Attention is a relevance filter. Imagine every token in a sentence is looking at every other token and asking " Hey, how much should i care about you?" When the model processes a word say "bank", it looks around neighbors like "river" or "money" to decide which definition holds true. This looking around is calculated via Attention scores. To compute this mechanically, we use three vectors - Query (Q): What the current token is looking for (e.g. "I am a verb, looking for my subject"). Key (K): What every other token identifies as (e.g. "I am a noun/subject"). Value (V): The actual content/meaning of the token. The "Attention" is just the match between the Query and the Key. If they match well, the model pays attention and absorbs the Value. This would also explain why Large Language Models are so good at long-range context and interpretation compared to previous architectures. 2 very good resources I recommend for understanding this better 1) Building LLMs from Scratch by Sebastian Raschka, PhD - Chapter 3 Coding Attention Mechanism. He also has a Youtube series on this but the book chapter is much more detailed. 2) Youtube tutorial by 3Blue1Brown - The animations are the best that I have seen in explaining attention. Follow up Question - Why is the square root of dk required in the Attention calculation? Answer in comments. Happy Learning #LLMs #GenAI #DataScience #MachineLearning #interview

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Most of us may have faced one question on Bayesian inference (formula below) in our data science interview where we needed to find out the posterior, given the other 3 metrics. p(theta | D) = p(D | theta) × p(theta) / p(D) , p(D) = ∫ p(D | theta) × p(theta) d theta Simple and straight, right. In a few special cases, it is also computationally complete. For example, with conjugate models (Normal prior + Normal likelihood), the posterior stays Normal, the denominator integral can be solved analytically, and we get closed-form expressions for posterior mean and variance. But this is textbook Bayesian inference. In most real ML problems - logistic likelihoods, nonlinear models, hierarchical structures, latent variables, or high-dimensional parameters - the denominator p(D) becomes intractable. The posterior, which combines prior belief with data evidence still exists, but we cannot normalize it, integrate it, or compute expectations directly. That’s why Bayes formula isn’t the end. That gap is why modern Bayesian inference relies on optimization and sampling methods. Variational Inference (VI): Bayes as Optimization - Instead of computing the true posterior p(theta | D), we choose a simpler distribution q(theta) (often Gaussian) and make it as close as possible to the posterior by minimizing KL divergence: KL( q(theta) || p(theta | D) ) This seems impossible because the posterior contains the intractable denominator. The key insight is rewriting the objective into the Evidence Lower Bound (ELBO): log p(D) = ELBO(q) + KL(q || p) Since KL ≥ 0, maximizing ELBO is equivalent to minimizing KL - without computing p(D). - q(theta) is our approximate posterior - ELBO maximization adjusts its parameters - Expected data fit + prior regularization − uncertainty penalty VI replaces difficult integrals with gradient-based optimization. It is fast and scalable, but accuracy depends on how expressive q(theta) is and often underestimates uncertainty. MCMC: Bayes as Sampling - Instead of approximating the posterior, it samples from it. In Metropolis–Hastings: 1. Propose a new value theta* 2. Compute an acceptance ratio r = ( p(D | theta*) × p(theta*) ) / ( p(D | theta_t) × p(theta_t) ) 3. Accept with probability, alpha = min(1, r) Crucially, the denominator p(D) cancels out. If alpha is high, the move is accepted; if low, it may be rejected. Accepting occasional worse moves prevents the chain from getting stuck and guarantees correct long-run behavior. MCMC is asymptotically exact but computationally heavier and requires convergence diagnostics. - VI: optimization-based, fast, scalable, approximate - MCMC: sampling-based, slower, but theoretically exact Both exist for the same reason: The denominator in Bayes’ formula is usually impossible to compute. And so, Bayesian inference doesn’t stop at Bayes’ rule and the real-world inference begins. #Bayesian #Optimization #Sampling

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