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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In this episode, Sean sat down with Jack Godau to dive deep into the world of pseudoanonymization. Jack shared how pseudoanonymization differs from anonymization, explaining its value for maintaining data utility while complying with stringent regulations like GDPR.
In this episode we explore how certificates and TLS function, the inherent difficulties in managing internal TLS certificates, and why nearly every engineer has a horror story related to it.
In this episode, we sit down with Ori Rafael, CEO and Co-founder of Upsolver, to explore the rise of the lakehouse architecture and its significance in modern data management.