Abhishek Divekar

Abhishek Divekar

Senior Applied Scientist

Amazon

Abhishek Divekar is a Senior Applied Scientist in Amazon's International Machine Learning team. His work has driven over half a billion dollars in revenue growth for Amazon and led to the deployment of 1,000+ ML models worldwide. He has authored multiple papers at Tier-1 AI conferences, pioneering fundamental research in areas including Synthetic Dataset Generation, Retrieval-Augmented Generation, and LLM-as-a-Judge, while also leading major open-source scientific projects. Abhishek earned his MS in Computer Science from The University of Texas at Austin and holds a B.Tech. from VJTI, Mumbai.

Synthetic data is transforming the landscape of training foundational models such as GPTs and Stable Diffusion, by enabling the creation of diverse, privacy-conscious, and annotation-efficient datasets. In this illuminating session, we will trace the frontier of synthetic data generation. We'll discuss generative AI techniques that are reshaping industries, demonstrating how synthetic datasets created by LLMs, diffusion models, and hybrids can augment or even replace traditional human-curated data. We'll highlight the pitfalls of careless generation at scale, including the amplification of hallucinations and entrenched biases, and offer practical strategies for safeguarding data quality. You'll learn how to ground synthetic data in real-world contexts, leveraging distributional similarity metrics and LLM-as-a-Judge to reliably benchmark synthetic versus human data. Join us to discover how responsible synthetic data practices can drive a more robust, ethical, and innovative AI-powered future.

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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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