The Future of AI Operations: Insights from PwC AI Managed Services
MLOps.community
Efficiency, Risk Appetite, and Use-Case Prioritization
Rani and Demetrios discuss trade-offs between perfection and cost, risk appetites across use cases, and DR-like decision frameworks.
Rani Radhakrishnan is a Principal at PwC US, leading work on AI-managed services, autonomous agents, and data-driven transformation for enterprises.
The Future of AI Operations: Insights from PwC AI Managed Services // MLOps Podcast #345 with Rani Radhakrishnan, Principal, Technology Managed Services - AI, Data Analytics and Insights at PwC US.
Huge thanks to PwC for supporting this episode!
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// Abstract
In today’s data-driven IT landscape, managing ML lifecycles and operations is converging.
On this podcast, we’ll explore how end-to-end ML lifecycle practices extend to proactive, automation-driven IT operations.
We'll discuss key MLOps concepts—CI/CD pipelines, feature stores, model monitoring—and how they power anomaly detection, event correlation, and automated remediation.
// Bio
Rani Radhakrishnan, a Principal at PwC, currently leads the AI Managed Services and Data & Insight teams in PwC US Technology Managed Services.
Rani excels at transforming data into strategic insights, driving informed decision-making, and delivering innovative solutions. Her leadership is marked by a deep understanding of emerging technologies and a commitment to leveraging them for business growth.
Rani’s ability to align and deliver AI solutions with organizational outcomes has established her as a thought leader in the industry.
Her passion for applying technology to solve tough business challenges and dedication to excellence continue to inspire her teams and help drive success for her clients in the rapidly evolving AI landscape.
// Related Links
Website: https://www.pwc.com/us/managedservices
https://www.pwc.com/us/en/tech-effect.html
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Timestamps:
[00:00] Getting to Know Rani
[01:54] Managed services
[03:50] AI usage reflection
[06:21] IT operations and MLOps
[11:23] MLOps and agent deployment
[14:35] Startup challenges in managed services
[16:50] Lift vs practicality in ML
[23:45] Scaling in production
[27:13] Data labeling effectiveness
[29:40] Sustainability considerations
[37:00] Product engineer roles
[40:21] Wrap up
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