Podcasts · CyberSecurity Summary
Machine Learning Systems: Designs that scale
· 21 min
Episode Machine Learning Systems: Designs that scale
A comprehensive guide to building production-grade predictive applications using the reactive systems paradigm. Through the narrative of a fictional startup, the author illustrates common pitfalls of naive designs, such as system instability, lack of scalability, and cascading failures. The text advocates for a modular architecture that is responsive, resilient, elastic, and message-driven to ensure reliability under heavy loads. Key technologies highlighted include Scala, Akka, and Spark, which facilitate functional programming and distributed data processing. Readers learn to manage the entire lifecycle of a model, from data collection and feature generation to model publishing and real-world response. Ultimately, the source serves as a blueprint for transforming machine learning from a mere experimental technique into a robust, scalable software system. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cyber_security_summary Get the Book now from Amazon: https://www.amazon.com/Reactive-Machine-Learning-Systems-Smith/dp/1617293334?&linkCode=ll2&tag=cvthunderx-20&linkId=a0dd0c59bfe912a7e0614a154c6a7454&language=en_US&gaOptInStatus=true&ref_=as_li_ss_tl Discover our free courses in tech and cybersecurity, Start learning today: https://linktr.ee/cybercode_academy
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Transcript
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