Beyond the Model: Building Scalable, Responsible AI Systems скачать в хорошем качестве

Beyond the Model: Building Scalable, Responsible AI Systems

In this episode of the Data Science Salon Podcast, we sit down with Dushyanth Sekhar, Head of AI & Data Platforms – Enterprise Data Organization at S&P Global – an AI and ML expert with experience designing and deploying scalable, production-ready AI systems across the enterprise. Dushyanth shares his journey into AI, the challenges of building complex pipelines, and how to integrate responsible and ethical practices into machine learning workflows. Key Highlights: • Scaling AI Systems: How to design and deploy pipelines that handle real-time inference, multimodal data, and production-level demands. • Model Interpretability & Explainability: Strategies for making complex models understandable and accountable. • Optimizing AI for Real-World Impact: Balancing performance, robustness, and human oversight in AI systems. • Responsible AI Practices: Embedding ethics, fairness, and transparency in machine learning workflows. 🎧 Tune in to Episode 61 to hear Dushyanth Sekhar’s insights on bridging technical innovation with responsible AI practices, and learn how to build AI systems that deliver both accuracy and real-world value. Be sure to mark your calendars for the 9th annual DSS ATX on Feb 18, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE ENTERPRISE. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/austin/