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Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems. For broader adoption, this practice must (i) accommodate product engineers without ML backgrounds, (ii) support finegrain product-metric evaluation and (iii) optimize for product goals. To address shortcomings of prior platforms, we introduce general principles for and the architecture of an ML platform, Looper, with simple APIs for decision-making and feedback collection. Looper covers the end-to-end ML lifecycle from collecting training data and model training to deployment and inference, and extends support to personalization, causal evaluation with heterogenous treatment effects, and Bayesian tuning for product goals. During the 2021 production deployment Looper simultaneously hosted 440-1,000 ML models that made 4-6 million real-time decisions per second. We sum up experiences of platform adopters and describe their learning curve., Comment: 11 pages + references, 7 figures; to appear in KDD 2022

This paper presents the development of the architecture of Distributed Network Intrusion Detection System.

The reception and study of lunar material returned by the Luna 16 space station is described. The layout of a vacuum receiving chamber for working with material in a helium atmosphere is examined along with the main operations involved in extracting the material from the ampule and drill.