Using AI to operationalize AI

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It’s complex. Data pipelines are already more complex before the complexity of the LLM stack is added. Data engineering teams deal with multiple cloud services and often on-premises systems. For example, ServiceOps platform providers receive data from 20-25 different sources. The modern DataOps stack adds data integration tools, data warehouses, business intelligence tools, and now LLM components.

There are many things that can break in today’s data architecture. More complexity means more interdependencies between different components. At the same time, the stakes are higher than they were when data pipelines fed relatively static reports patiently accessed by a few internal employees. Today, data applications are fed by streaming data and are woven into the customer experience ecosystem.

However, teams working with LLM will not have to reinvent the wheel to support these new architectures in production. Some of the problems they will encounter will be old and fairly familiar, and some may be new variations on well-known themes common to the LLM universe.

For example, teams must ensure that data is properly denmark mobile database before it enters the model, and that governance, security, and observability are in place. Database availability is a common practice, even though vector databases may be new to many teams. Latency has always been an issue in data-intensive applications, but teams now also need to consider the impact of data freshness on LLM results. From a security perspective, we have long dealt with attack vectors such as SQL injection, and now we need to protect against hint injection.

In short, there are many valuable lessons and practices that can and should be applied to the non-functional aspects of LLM implementations, including DevOps, database and systems reliability engineering, and security. Follow best practices for testing, monitoring, vulnerability management, setting service level objectives (SLOs), and managing bug budgets to enable real-time changes. Do all of these things with the big picture in mind, and your LLM-based functions will be much more likely to deliver the promised business impact through high performance and availability.

Finally, it’s time for the technology part. The good news is that we can actually use AI to operationalize AI. In fact, it’s a must, given the complexity of the LLM application stack. There are things that computers are better at than humans, and we need to recognize that and leverage the capabilities of machines to improve efficiency.