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

• The AI workflow involves multiple personas, including data scientists, developers, and infrastructure administrators.
• Data scientists play a crucial role in developing AI models, while developers are responsible for deploying the models into production.
• Infrastructure administrators need to consider the virtualization layer and ensure efficient and easy consumption of infrastructure components.
• Deploying AI models at the edge requires quantization to reduce model size and considerations for form factor, scale, and connectivity.
• The pipeline for deploying models involves steps such as unit testing, scanning for vulnerabilities, building container images, and pushing to a registry.
• Unit testing focuses on testing individual components, while integration testing ensures the compatibility and functionality of the entire system. 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.

Chapters

• 00:00 Understanding the AI Workflow and Personas
• 03:24 The Role of Data Scientists and Developers in Deploying AI Models
• 08:47 The Responsibilities of Infrastructure Administrators
• 15:25 Challenges of Deploying Models at the Edge
• 20:29 The Pipeline for Deploying AI Models
• 24:45 Unit Testing vs. Integration Testing
• 28:22 Managing ML Models with MLflow and Other Tools
• 32:17 The Emergence of Smaller Models
• 39:58 Collaboration for Security and Governance in AI Projects
• 46:32 The Importance of Data Governance

Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

Summary
For this special edition of the podcast Duncan invited Michael Roy to discuss the latest VMware Workstation and VMware Fusion announcements. VMware Workstation and Fusion are desktop hypervisor products that allow users to run virtual machines on their PC or Mac. Starting today, Workstation and Fusion commercial licenses will only be available through annual subscriptions. The price for both products is now $199 per year. The free versions of Fusion Player and Workstation Player are being discontinued, but the Pro versions will be available for free for personal use. Support for personal use products will be community-based, while commercial users will have support included in their subscription. The focus of future innovation will be on the integration between vSphere and Workstation/Fusion, providing a local virtual sandbox for learning, development, and testing.

Takeaways

• VMware Workstation and Fusion are desktop hypervisor products for running virtual machines on PC and Mac.
• Commercial use of Workstation and Fusion is shifting from perpetual licenses to annual subscriptions.
• The free versions of Fusion Player and Workstation Player are being discontinued, but the Pro versions will be available for free for personal use.
• Support for personal use products will be community-based, while commercial users will have support included in their subscription.
• Future innovation will focus on integrating vSphere with Workstation and Fusion to provide a local virtual sandbox for learning, development, and testing.

Links

• Announcement Blog
• The Register article

Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

• The CNCF aims to make cloud native technology ubiquitous by providing support and resources to cloud native projects.
• Using CNCF projects allows IT admins and platform engineers to build integrated and efficient platforms.
• Platform engineering and AI/ML workloads are future trends within the CNCF.
• Calculating the return on investment for platform engineering practices is important.
• William Rizzo is working on a project to simplify the calculation of return on investment for platform engineering practices.

Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.