When it comes to today’s AI systems, especially in Generative AI, the main challenge isn’t just building a basic system with, say, 70% accuracy—it’s about pushing that system to over 90% and making it reliable for real-world production. Optimizing RAG (Retrieval-Augmented Generation) systems is essential for reaching this level. Effective chunking, one of the foundational … Continue reading Optimizing RAG Systems: A Deep Dive into Chunking Strategies.
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Understanding RAG and Vector DB
For anyone in the field of AI and ML, they probably do know all of these terms. But how do you explain these to someone new. Let's try understanding this. USE CASE: Mark wants to plan a trip to Georgia in Europe and he wants to use an LLM for this. INTERACTION WITH LLM: Attempt … Continue reading Understanding RAG and Vector DB
AI Use Case Template – 30,000 ft
Over the last few years having worked on the production side of AI I have had the experience working with a few AI/ML initiatives. Some have made it to production and a lot have not made it to production. Failures happen at multiple stages :- Inception stage: idea proposed from products team and dropped for … Continue reading AI Use Case Template – 30,000 ft
ML Monitoring In Production- Why and What(30,000 ft)
Why to Monitor? What is the goal of an ML system for a business use case? Do you think it is below? i.e Designing an ML system which has the best model accuracy Not exactly. The goal looks more like below True ROI from an ML system is only achieved by monitoring the system in … Continue reading ML Monitoring In Production- Why and What(30,000 ft)
Understanding AI/ML Systems In Production
AI and ML have evolved from complex mysteries few years back to everyday tools, driven by models like ChatGPT and Bard. ChatGPT and Bard made AI easier, but most are still basic because turning them profitable needs rare know-how mostly in big companies. It's not just about Language Models; AI's larger development faces this. Many … Continue reading Understanding AI/ML Systems In Production
Machine Learning in production- An overview
So this article started with a Google search a while back about ML and it was the percentage of ML models that made it to productionThis actually got me interested. I mean, I do this exact thing for work. I work as a software developer in the data science team of an Ecomerce company.So this … Continue reading Machine Learning in production- An overview
Big Data Engineer vs Machine Learning Engineer vs Data Scientist- Questions that helped me choose…
Well, this is actually something quite interesting. And I am sure a lot has been said about these roles on the web and this post is mostly for folks who just started out or are starting, and generally, there are three sorts of people:Geetha: Geetha is Someone who is already in the field looking to … Continue reading Big Data Engineer vs Machine Learning Engineer vs Data Scientist- Questions that helped me choose…
AI For Production 