Sadhana Aluguri

It has been fun putting together the Data Engineering track for Fifth Elephant Conference this year along with Shashwat Agarwal and Pramod Biligiri #DataManagement, #MachineLearning, #AI & #Data We have assembled a diverse set of talks.   Even if you are not a hard-core data engineer, you will find something of interest A few talks to highlight 1. Vishnu Vasanth from e6data will talk on the Lakehouse Compute engine they have built. 2. Ruturaj Bhokre from Dream11 will talk on the Evolution of their data platform 3. Mohd. Shadab S. (CISO at Hotstar) will explain Design patterns in Data security 4. Niraj Mishra from Atlassian will talk on Ephemeral data pipelines You may know of API contracts but have you heard of Data Contracts ? Bichitra Kumar Sahoo, Rahul Madan and Shikhar Vaish from Atlan will be conducting a workshop on Data contracts and OpenLineage. There is a separate "LLM and GenAI" track as well, if that suits your interest more. The Fifth Elephant 2024 Conference is on: 📅 13 July 2024 (Saturday)  ⏰ 9 AM to 6 PM  📌 BIC, Indiranagar, Bangalore Hybrid Access Check out the schedule here & go grab your early elephant tickets today at: http://has.gy/Xz_w P.S. Tag anyone in your networks who is working in the space of #AI #DataScience & #LLMs

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This is a game-changer for optimizing RAG setups! 🔧 With so many variables to consider, manually finding the best configuration can indeed be overwhelming. The introduction of RAGBuilder and its hyperparameter tuning capabilities simplifies the process significantly, allowing for efficient discovery of the optimal setup. Excited to check it out and see how it integrates with SingleStore for a streamlined RAG setup. Thanks for sharing this valuable tool! #RAG #MachineLearning #DataScience #AI #HyperparameterTuning #OpenSource #TechInnovation #SingleStore

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I just completed the first chapter of this amazing book Designing Machine Learning Systems by Chip Huyen and I realised how much ground work has went through to just create the first chapter and I am yet go further. She has explained so well the difference between ML in academics and ML in Production. I won’t go into the details, for that you need to read the book. But I remember a recent incidence where we got a project to summarise tables using GenAI and I, naively, suggested that we can just converted all rows into text and stored it in vector database and from there we can created a RAG based Chatbot. It is simple and I showed the results using a sample table and the results were also good. However, an experienced colleague from engineering team said, “Do you know the size of dataset, and do you know the cost of vector db to store it”. The table size was huge and to store it in vector db will also be huge. So, we as a Data Scientist not only need to deliver the results but do need to take care of lot of factors and this book pin points all. I would recommend all the budding Data Scientist to read this book, it will help you a lot in your journey as a Data Scientist.

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