Machine learning (ML) is the catalyst to a strong, flexible, and resilient business in today’s market. Smart companies leverage ML to create top-to-bottom growth, employee productivity, and customer satisfaction. These firms have achieved success with a few ML use cases, but they can’t stop there. Experimenting with ML is the easy part. The harder part is integrating ML models into business applications and processes to scale ML across the enterprise. Currently, most firms lack the skills, processes, and tools to accomplish this.
In order to successfully achieve ML at scale, firms must invest in MLOps — the process, tools, and technology that streamline and standardize each stage of the ML lifecycle from model development to operationalization.