Andrew Ng: "Deploying to production means you're halfway there."

Andrew Ng, a prominent figure in AI, discussed the shift from "big data" to "good data" at the Scale Transform conference, emphasizing the iterative nature of machine learning projects. He highlighted that many enterprise AI projects fail due to a lack of understanding of AI as a continuous feedback loop, where real-world data is crucial for improving models over time. Ng pointed out that data quality, not just quantity, is essential for AI models, comparing it to a balanced diet, and stressed that data cleaning occupies a significant portion of a machine learning engineer's work. He advised focusing on data improvements over tweaking model architectures, as the former often yields faster performance gains. Ng also underlined the importance of systematic and repeatable processes over ad-hoc methods like using Jupyter notebooks, advocating for production-grade tooling to ensure reliable AI systems. Platforms like Roboflow are designed to support this iterative process, helping organizations build proof of concept models, instill robust data practices, and continually enhance their projects with active learning.