Rewired

Chapter 23 MLOps so AI can scale

Building advanced AI is like launching a rocket. The first challenge is to maximize acceleration, but once it starts picking up speed, you also need to focus on steering.

—Jaan Tallinn

For AI/ML to make a sizable contribution to a company's bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations in real time. This is particularly difficult with AI/ML models because they are “living organisms” that change with the underlying data. They require constant monitoring, retraining, and debiasing - a challenge with even a few ML models but simply overwhelming with hundreds of them.

Defining key terms

• Artificial intelligence (AI) covers the broad concept of creating smart intelligent machines.
• Machine learning (ML) is a subset of artificial intelligence. It's a method that “learns” from data to improve performance on some set of tasks.
• Deep learning is a subset of machine learning. It uses vast volumes of data and complex algorithms to train a model.

In recent years, massive improvements in ML tooling and technologies have dramatically transformed ML workflows, expedited the application life cycle, and enabled consistent and reliable scaling of AI across business domains. With all these new capabilities, however, the key point to remember is that effective ML operations (MLOps) requires a focus on the full ...