Open source frameworks such as Spark, TensorFlow, MXNet, and PyTorch enable anyone to model and train deep learning models. While there are many great tutorials and talks showing the best way to train models, there’s little information on what happens before and after training your model—in other words, how to develop, store, utilize, test, and refine it.
Tobias Knaup and Joerg Schad offer an introduction to building a complete automated deep learning pipeline, starting with exploratory analysis, overtraining, model storage, model serving, and monitoring.
Tobi Knaup is the cofounder and CTO at Mesosphere, a hybrid cloud platform company that helps companies such as NBCUniversal, Deutsche Telekom, and Royal Caribbean adopt transformative technologies like machine learning and real-time analytics with ease. He was one of the first engineers and tech lead at Airbnb, where he wrote large parts of the company’s infrastructure, including its search and fraud prediction services, and helped scale the site to millions of users and build a world-class engineering team. Tobi is the main author of Marathon, Mesosphere’s container orchestrator.
Jörg Schad is Head of Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He’s a frequent speaker at meetups, international conferences, and lecture halls.
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