In this conversation, Myles Gray discusses the AI workflow and its personas, the responsibilities of data scientists and developers in deploying AI models, the role of infrastructure administrators, and the challenges of deploying models at the edge. He also explains the concept of quantization and the importance of accuracy in models. Additionally, he talks about the pipeline for deploying models and the difference between unit testing and integration testing. Unit testing is used to test the functionality of a single module or function within an application. Integration testing involves testing the interaction between different components or applications. MLflow and other tools are used to store and manage ML models. Smaller models are emerging as a solution to the resource constraints of large models. Collaboration between different personas is important for ensuring security and governance in AI projects. Data governance policies are crucial for maintaining data quality and consistency.
Takeaways
Chapters
Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.
In this conversation, Myles Gray discusses the AI workflow and its personas, the responsibilities of data scientists and developers in deploying AI models, the role of infrastructure administrators, and the challenges of deploying models at the edge. He also explains the concept of quantization and the importance of accuracy in models. Additionally, he talks about the pipeline for deploying models and the difference between unit testing and integration testing. Unit testing is used to test the functionality of a single module or function within an application. Integration testing involves testing the interaction between different components or applications. MLflow and other tools are used to store and manage ML models. Smaller models are emerging as a solution to the resource constraints of large models. Collaboration between different personas is important for ensuring security and governance in AI projects. Data governance policies are crucial for maintaining data quality and consistency.
Takeaways
Chapters
Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.