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Manoranjan Rajguru

AI Global Black Belt

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Manoranjan Rajguru is an AI Global Black Belt at Microsoft, specializing in Generative AI and Large Language Models. With over a decade of experience in AI and ML, he previously served as a Data Scientist at Amazon. Manoranjan is a prominent figure in the tech community, contributing over 30 articles to Medium and maintaining a LinkedIn following of 8,000 professionals. His expertise and thought leadership have significantly influenced technical advancements and shaped the AI landscape. Manoranjan's work continues to drive innovation and impact in the field of artificial intelligence.

The session focused on the advancements in NLP with Retrieval-Augmented Generation (RAG) models, highlighting their ability to combine information retrieval and generative language models for context-rich answers. It stressed the importance of incorporating image understanding and hierarchical document structure analysis to manage the visual data that accompanies textual information. The session provided an implementation guide using Azure Document Intelligence to convert images to markdown and recognize document structure, focusing on visual feature extraction, visual semantics, multimodal data fusion, structure recognition, semantic role labeling, and structure-aware retrieval. It also detailed the setup of Azure services and models, including GPT-4-Vision-Preview and Azure Document Intelligence, and offered a walkthrough for utilizing these tools.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

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