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Mastering GenAI Data Strategy

Hosted by Mahesh Yadav

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What you'll learn

GenAI PM's Role in Data Strategy & Preparation

How to create a data strategy, and align with stakeholders to minimize cost for training.

Critical Components in Data Strategy

Framework for developing your data strategy including data source, data governance, and maintaining data quality.

How to Navigate Key Challenges in Data Preparation

Practical tips to mitigate challenges in data acquisition, data quality, ethical considerations, and resource allocation

Why this topic matters

As GenAI products become mainstream, differentiating your product from vanilla base models like GPT-4, Gemini, and Llama become crucial. A strong data strategy will help you build a GenAI product with true market differentiation. The key to building a moat in a GenAI product lies in how much relevant data you have for your Critical User Journey (CuJ).

You'll learn from

Mahesh Yadav

GenAI Product Lead at Google, previously at Meta, Amazon, and Microsoft.

Mahesh Yadav is a Product Leader at Google GenAI team. Mahesh is one of the world's top AI executives and an award-winning AI Product Educator. His work on AI has been featured in the Nvidia GTC conference, Microsoft Build, and Meta blogs.


Mahesh has years of experience in building products at Meta, Microsoft and AWS AI teams. Mahesh has worked in all layers of the AI stack from AI chips to LLM and has a deep understanding of how GenAI companies ship value to customers. Currently, he leads AI agent at Google Cloud where it is used extensively for Gemini and other key Google products.


PRIVIOUSLY AT

Microsoft
Meta
Amazon Web Services

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