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  • Designing the Jit Analytics Architecture for Scale and Reuse

    As a SaaS provider, analytical data at Jit needs to be useful to both their customers and to internal stakeholders. AWS services including EventBridge, Kinesis Data Firehose, and Timestream handle data ingestion and UI platforms from Mixpanel and Segment provide data visualization.

  • Designing IoT Solutions with Microsoft Azure

    In this article, we will learn how the IoT solutions can work with Microsoft Azure and what services are available to perform different operations across multiple domains. Furthermore, it covers a few case studies to gain hands-on experience on Azure IoT that are common and provide a good starting point for utilizing cloud-based IoT services.

  • Monitoring Microservices the Right Way

    Modern systems are more complex to monitor as they tend to emit large amounts of high cardinality data. Recent innovations in open-source time series databases have improved the scalability of newer monitoring tools such as Prometheus. These solutions are able to handle the high scale of data while providing metric scraping, querying, and visualization based on Prometheus and Grafana.

  • Predicting Time to Cook, Arrive, and Deliver at Uber Eats

    Time predictions are critical to Uber Eats' business as they determine when to dispatch delivery partners as well as ensure customer satisfaction. This article explains how their dispatch system evolved through time predictions powered by machine learning, followed by a deep dive on how to predict food preparation time without ground truth data. It goes over delivery and travel time predictions.

  • Understanding Software System Behaviour with ML and Time Series Data

    David Andrzejewski presented "Understanding Software System Behaviour with ML and Time Series Data". This article is a summary of his presentation and an overview on what to look out for. Know about the traditional approaches to time series, how to handle missing values, and know about possibly occurring seasonality in your data. Be careful about what threshold you set for anomaly detection.