In this episode, we dive into the world of MLOps, the engine behind secure and reliable AI/ML deployments. MLOps focuses on the lifecycle of machine learning models, ensuring they are developed and deployed efficiently and responsibly.
With the explosion of ML applications, the demand for specialized tools has skyrocketed, highlighting the need for improved observability, auditing, and reproducibility. This shift necessitates an evolution in ML toolchains to address gaps in security, governance, and reliability.
Jozu is a platform founded to tackle these very challenges by enhancing the collaboration between AI/ML and application development teams. Jozu aims to provide a comprehensive suite of tools focusing on efficiency throughout the model development and deployment process.
This conversation discusses the importance of MLOps, the limitations of current tools, and how Jozu is paving the way for the future of secure and reliable ML deployments.
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Aaron Painter, CEO of Nametag, joins the show to explore the evolving threat of AI deepfakes. They discuss the increasing sophistication of deepfake technology, highlighted by the significant rise in incidents such as the Retool hack, and how these technologies can manipulate public perception and security.
In this episode we’re joined by Shubh Sinha, CEO and Co-founder of Integral, to discuss the protection and utilization of sensitive health data.
In this episode, we dive deep into the world of prompt injection attacks in Large Language Models (LLMs) with the Devansh, AI Solutions Lead at SVAM. We discuss the attacks, existing vulnerabilities, real-world examples, and the strategies attackers use.