How to Evaluate Different Machine Learning Deployment Solutions

Reach out to us at deployML@wallaroo.ai for a free evaluation. The emergence of Big Data in decision-making to achieve strategic business objectives has led to machine learning (ML) becoming a key enabler for driving growth, achieving operational excellence, and bringing innovative products to market. This shift has come about as the primary obstacles for ML are being overcome: data engineering at scale and model development are no longer daunting to enterprises given the many efficient and simple solutions provided by cloud or 3rd-party vendors. As a result, ML went from something only the bleeding edge innovators (such as Netflix and Amazon) were doing, to now a strategic enabler for organizations in the "early majority" stage of adoption. However, enterprises soon find that building a machine learning model isn't the end of the road but just the beginning of a new set of challenges: Because this is all so new, most enterprises do not have a pre-defined set of parameters to evaluate the different solutions for operationalizing ML models. As a result, they are not sure which attributes will allow their AI-enabled products and operations to scale in the long term as they add more models, use more data, or build more complex models.