Many data scientists and ML engineers have faced the challenge of putting AI models into production, and this is the core of MLOps. In this episode, Adam Probst, Co-Founder of ZenML, joins Frederic Van Haren and Stephen Foskett to discuss the challenges of putting ML models into production. Machine learning pipelines are inherently complex and fragile and require feedback and tuning, and this requires a new approach with continuous improvement and tight integration. Although reminiscent of DevOps, MLOps demands even more collaboration between IT operations, developers and data scientists, and lines of business. ZenML prepares ready-to-use MLOps infrastructure to these groups so they can focus on the model rather than the platform.
- Stephen: How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?
- Frederic: Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Zach DeMeyer: What’s the most innovative use of AI you’ve seen in the real world?
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