Catherine Dong

Major tech companies have actively reoriented themselves around AI and machine learning: Google is now “AI-first,” Uber has ML running through its veins and internal AI research labs keep popping up.

They’re pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants and more.

Despite this hype around the state of the art, the state of the practice is less futuristic.

Software engineers and data scientists working with machine learning still use many of the same algorithms and engineering tools they did years ago.

That is, traditional machine learning models — not deep neural networks — are powering most AI applications. Engineers still use traditional software engineering tools for machine learning engineering, and they don’t work: The pipelines that take data to model to result end up built out of scattered, incompatible pieces. There is change coming, as big tech companies smooth out this process by building new machine learning-specific platforms with end-to-end functionality.