For AI model success, utilize MLOps and get the data right

It’s critical to adopt a data-centric mindset and support it with ML operations.

Artificial intelligence (AI) in the lab is one thing; in the real world, it’s another. Many AI models fail to yield reliable results when deployed. Others start well, but then results erode, leaving their owners frustrated. Many businesses do not get the return on AI they expect. Why do AI models fail and what is the remedy?

As companies have experimented with AI models more, there have been some successes, but numerous disappointments. Dimensional Research reports that 96% of AI projects encounter problems with data quality, data labeling and building model confidence.

AI researchers and developers for business often use the traditional academic method of boosting accuracy. That is, hold the model’s data constant while tinkering with model architectures and fine-tuning algorithms. That’s akin to mending the sails when the boat has a leak — it is an improvement, but the wrong one. Why? Good code cannot overcome bad data.

Instead, they should ensure the datasets are suited to the application. Traditional software is powered by code, whereas AI systems are built using both code (models + algorithms) and data. Take facial recognition, for instance, in which AI-driven apps were trained on mostly Caucasian faces, instead of ethnically diverse faces. Not surprisingly, results were less accurate for non-Caucasian users.

Good training data is only the starting point. In the real world, AI applications are often initially accurate, but then deteriorate. When accuracy degrades, many teams respond by tuning the software code. That doesn’t work because the underlying problem was changing real-world conditions. The answer: to increase reliability, improve the data rather than the algorithms.

Since AI failures are usually related to data quality and data drifts, practitioners can use a data-centric approach to keep AI applications healthy. Data is like food for AI. In your application, data should be a first-class citizen. Endorsing this idea isn’t sufficient; organizations need an “infrastructure” to keep the right data coming.

MLOps: The “how” of data-centric AI

Continuous good data requires ongoing processes and practices known as MLops, for machine learning (ML) operations. The key mission of MLops: make high-quality data available because it’s essential to a data-centric AI approach.[…]

It’s critical to adopt a data-centric mindset and support it with ML operations.

MLOps: The “how” of data-centric AI

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