Move Fast Without Breaking Things in ML

In this piece, Bob and Aparna discuss the importance of reliability engineering for ML initiatives. Machine learning is quickly becoming a key ingredient in emerging products and technologies. This has caused the field to rapidly mature as it attempts to transform the process of building ML models from an art to an engineering practice. In other words, many companies are learning that bringing a model that works in the research lab into production is much easier said than done. One particular challenge that ML practitioners face when deploying models into production environments is ensuring a reliable experience for their users. Just imagine, it's 3 am and you awake to a frantic phone call.