Machine learning (ML) has demonstrated success in various domains such as web search, ads, computer vision, natural language processing (NLP), and more. These success stories have led to a big focus on democratizing ML and building robust systems that can be applied to a variety of domains, problems, and data sizes. However, due many times to poor understanding of typical ML algorithms, an expert tries a lot of hit-and-miss efforts to get the system working, thus limiting the types and applications of ML systems. Hence, designing provable and rigorous algorithms is critical to the success of such large-scale, general-purpose ML systems. The goal of this session is to bring together researchers from various communities (ML, algorithms, optimization, statistics, and more) along with researchers from more applied ML communities such as computer vision and NLP, with the intent of understanding challenges involved in designing end-to-end robust, rigorous, and predictable ML systems.