AI Deployment Simplified: Kit Ops' Role in Streamlining MLOps Practices - ML 159

In today’s episode, they dive into the intricate world of MLOps with Brad Micklea, a seasoned expert with extensive experience in software infrastructure and leadership roles at Eclipse Shay, Red Hat, AWS, and Jozu. Brad shares his journey of founding Jozu, an MLOps company that stands out with its commitment to open standards such as the OCI standard for packaging AI projects. Alongside Jozu, they explore KitOps, an innovative open-source project that simplifies version control and collaboration for AI teams.

Special Guests: Brad Micklea

Show Notes

In today’s episode, they dive into the intricate world of MLOps with Brad Micklea, a seasoned expert with extensive experience in software infrastructure and leadership roles at Eclipse Shay, Red Hat, AWS, and Jozu. Brad shares his journey of founding Jozu, an MLOps company that stands out with its commitment to open standards such as the OCI standard for packaging AI projects. Alongside Jozu, they explore KitOps, an innovative open-source project that simplifies version control and collaboration for AI teams.

Join them as they discuss the challenges in integrating AI models into production, the importance of monitoring API usage, and the critical role of automated rollback systems in maintaining operational excellence. They also touch on the cultural differences in operational approaches between giants like AWS and Red Hat and hear first-hand experiences on the significance of transparency, trust, and efficient risk management in both startups and established companies.

Whether you're a DevOps professional, MLOps practitioner, or data scientist transitioning to production, this episode is packed with valuable insights and practical advice to help you navigate the complexities of AI project management. Tune in to discover how Brad and his team are tackling these challenges head-on and learn how to set up your projects for success from the ground up!

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AI Deployment Simplified: Kit Ops' Role in Streamlining MLOps Practices - ML 159
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