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.

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, training, tuning, hosting, explainability, monitoring, and workflow automation. You can use a variety of techniques to deploy new ML models to production, so choosing the right strategy is an important decision. You must weigh the options in terms of the impact of change on the system and on the end users. In this post, we show you how to deploy using a shadow deployment strategy.