It is well-known that machine and deep learning algorithms are usually data-dependent. But how well does your data reflect the real world? While technology companies with massive data troves can be more certain that their data is representative, having high-quality information on a particular issue allows companies with smaller datasets to gain from AI.
Computer scientist and co-founder of Coursera, Andrew Ng, recommends collecting the right data rather than creating a custom AI system: “companies that are faster to adopt a data-centric approach to AI will have a leg up relative to competitors.” A data-centric approach shifts talent from developing bespoke models toward building data pipelines.
For a business to collect the kind of data that an AI model can use, significant infrastructure needs to be built around the algorithms. New machine learning operations – or MLOps – tools are designed to help produce these high-quality datasets. MLOps refers to all the engineering pieces that come together and often help to deploy, run, and train AI models. While businesses are still competing for AI experts in a red hot tech talent market, effective MLOps tools can make AI deployment easier, more efficient, and more accessible to companies with smaller data sets.
As AI becomes a ubiquitous tool for decision-making in business, the winners may not have the most data but will have a strong grasp on why they have collected data and what they want an AI to learn from it.