Practical AI MLOps is NOT Real
5 snips
Apr 26, 2022 Luis Ceze, CEO of OctoML, dives into the fascinating intersection of MLOps and DevOps. He discusses the need to treat AI/ML models as regular software components once trained. The conversation touches on automating model deployment, the growth of model hubs like Hugging Face, and how community engagement drives advancements. Ceze also highlights the challenges of model optimization and the crucial role of user-friendly solutions for data scientists. It's a thought-provoking look at the future of machine learning and operational practices.
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ML Models as Software
- Machine learning models are integral to modern intelligent applications, yet treated differently than other software.
- This special treatment hinders the development of these applications, necessitating a shift in perspective.
Automate Model Export
- Automate the process of exporting models into well-defined containers.
- This allows data scientists to focus on model creation and DevOps teams on deployment and integration.
Automation for ML Deployment
- Automating vulnerability analysis in software development has significantly improved code security.
- A similar automation approach in machine learning faces a wider gap between model creators and deployers, requiring deeper automation.