Speaker: Abhinav Tewari – Manager, Aristocrat Technologies
Description
This session explores how enterprises can effectively deploy, scale, and govern both applied and generative AI models. It delves into the strategic integration of proprietary data, the balance between data-centric and model-centric development, and the role of MLOps in ensuring reproducibility and reliability. Attendees will gain insights into the latest advancements in transformers, AutoML, and ethical AI governance from a business-first perspective.
Key Takeaways:
• Tailoring AI with Proprietary Data: Discover how organizations can adapt generative and predictive models using internal data assets to unlock unique business value.
• Strategic AI Development: Data vs. Model Focus: Gain insights into when to prioritize data quality versus model architecture, and how each approach impacts outcomes.
• Enterprise-Grade MLOps: Understand how MLOps frameworks support scalable, reliable, and reproducible AI workflows across the enterprise.
• Ethical and Sustainable AI Deployment: Explore best practices for managing model drift, ensuring transparency, and embedding ethical governance into AI systems at scale
