Weekly Digest #127

Articles

How Meta measures the management of its AI ecosystem

• Think about how different tools contribute to a generic AI development process, and map outputs from various tools to consistent artifacts in that process.
• If your AI systems are complex and can’t be consolidated, think of measurement as a platform with a metadata graph and common interoperability standards.
• For a broad view of outcomes, consider a metric framework that can compare and aggregate across model management domains.

What complex systems can teach us about building software

1. Define a set of quality architecture principles that the organization believes in and that are systematically enforced. These are the immutable rules governing how individual components within the system behave.
2. Develop quality information aggregation systems that allow leaders to determine which components of the system are following the rules, which ones are succeeding and should be nurtured through incentives, and which ones are failing and should be killed.
3. Experiment widely and encourage diversity in how we build teams and how we build software. The more broadly we experiment and learn, the more chance we have of breakthroughs that can evolve into the behaviour we want.