- AI is most valuable when it is operationalized at scale
- For business leaders who wish to maximize business value using AI, scale refers to how deeply and widely AI is integrated into an organization’s core product or service and business processes
- Scaling isn’t easy
- One or two AI models into production is very different from running an entire enterprise or product on AI
Processes: Standardize how you build and operationalize models
- The first step to making AI scale is standardization: a way to build models in a repeatable fashion and a well-defined process to operationalize them. To standardize, organizations should collaboratively define a “recommended” process for AI development and operationalization, and provide tools to support the adoption of that process.
Governance
- With AI and ML, governance becomes much more critical than in other applications.
- As a result, it becomes essential for any MLOps tool to bake in practices for responsible and ethical AI including capabilities like “pre-launch” checklists for responsible AI usage, model documentation, and governance workflows.
People: let teams focus on what they’re best at
- To successfully scale AI, business leaders should build and empower specialized, dedicated teams that can focus on high-value strategic priorities that only their team can accomplish
- Let data scientists do data science; let engineers do the engineering; let IT focus on infrastructure
Whether it’s friendly for data science as well as IT
- Data scientists build models, IT teams maintain the AI Infrastructure and run AI models in production, and governance teams oversee the use of models in regulated scenarios.
- To enable data scientists to do their best work, a platform must get out of the way
- An ideal MLOps Platform can do both
