AI Doesn't Have to Be Too Complicated or Expensive for Your Business
For most companies that are interested in using AI, there isn’t a clear model to follow. The approach to building AI used by massive internet companies like Amazon and Google just doesn’t translate — most companies don’t have overflowing troves of data they can use to train models. So, industries such as manufacturing, agriculture, and healthcare need to take a different approach: programming with data, not code. Companies in these industries typically have relatively small data sets, face high costs for customizing a system, and are scared off by long gaps between pilot and product. But, given advances in AI technology, these organizations should shift their focus from building the right model — a software-focused approach — to focusing getting good data, which clearly illustrates the concepts we need the AI to learn, and using new machine learning operations (MLOps) tools. These tools that are geared to help produce high-quality datasets, in particular, hold the key to addressing the challenges of small datasets, high cost of customization, and the long road to getting an AI project into production outlined above. Companies should focus on gathering high-quality data, shifting the focus of their engineering corps away from model-centric approaches, and make the deployment process and MLOps tools needed to support it a central part of the planning project for any AI project.
Read Full Article Here: https://hbr.org/2021/07/ai-doesnt-have-to-be-too-complicated-or-expensive-for-your-business