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.
- Asked by Stephen: How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?
- Asked by Frederic: Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Asked by Zach: What’s the most innovative use of AI you’ve seen in the real world?
Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren .
Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day . Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
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