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Aditya Rane

Cloud Migration Consultant AI - ML

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Aditya has a decade of professional experience in product development, consulting, and IT services. He has delivered multiple high-impact AI solutions with cross-cultural teams across geographies. Aditya is also a contributor to open-source software projects like Langchain. He is also passionate about teaching AI and has trained more than 1000 students and working professionals across India and South East Asia.

Prepare for the ultimate AI showdown at this year’s DataHack Summit! We're bringing together 3 AI experts to tackle one challenging case problem using three of the hottest techniques in the field: Retrieval-Augmented Generation (RAG), Long Context Language Models, Fine-Tuning  and combining these approaches. This is your chance to see these cutting-edge methods go head-to-head and understand their unique strengths and weaknesses.

It offers a rare opportunity to compare and contrast different problem-solving approaches, learn from leading experts, and expand your understanding of how versatile and dynamic the field can be.

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Gemini is Google's most capable model. It is built from the ground up for multimodality, seamlessly combining and understanding text, code, images, audio, and video. We begin the session with a coding demo of Gemini's multimodal capabilities using Google Cloud Vertex AI. We also demonstrate building agents using Gemini. We demonstrate context-switching and tool-calling capabilities. We proceed with a demo of Gemini's ability to process large inputs. We conclude the session by building a Multimodal RAG application using Gemini. 

 

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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

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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

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