InfoQ Homepage Model Content on InfoQ
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Deep Learning at Scale: Distributed Training and Hyperparameter Search for Image Recognition Problems
Michael Shtelma discusses methods and libraries for training models on a dataset that does not fit into memory or maybe even on the disk using multiple GPUs or even nodes.
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From Spark to Elasticsearch and Back - Learning Large-Scale Models for Content Recommendation
Sonya Liberman shares an algorithmic architecture that enables running complex models under difficult scale constraints and shortens the cycle between research and production.
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ML's Hidden Tasks: A Checklist for Developers When Building ML Systems
Jade Abbott discusses the set of unexpected things that go on the "take it to production" checklist in the case of machine learning, and what are the tools that can help.
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A Look at the Methods to Detect and Try to Remove Bias in Machine Learning Models
Thierry Silbermann explores some examples where machine learning fails and/or is making a negative impact, looking at some of the tools available today to fix the model.
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Deep Learning for Recommender Systems
Oliver Gindele discusses how some DL models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems.
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Petastorm: A Light-Weight Approach to Building ML Pipelines
Yevgeni Litvin describes how Petastorm facilitates tighter integration between Big Data and Deep Learning worlds, simplifies data management and data pipelines, and speeds up model experimentation.
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Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
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BDD and the New Model for Testing
Paul Gerrard proposes a model of the thought processes that every tester uses which maps directly to the BDD way, helping practitioners understand the BDD collaboration and test process.
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Test-Driven Machine Learning
Detlef Nauck explains why the testing of data is essential, as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment.
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Keep It Simple, Stupid: Driving Model Adoption through Tiers
Jamie Warner covers a tiered approach to model introduction and implementation that focuses on building stakeholder buy-in without abandoning advanced techniques.
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Creating High-Performance Teams Using the Human Full Stack
James Brett and Marina Chiovetti discuss the human elements that impact a team’s ability to create and respond to disruption using the Human Full Stack model.
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Deep Learning for Application Performance Optimization
Zoran Sevarac presents his experience and best practice for autonomous, continuous application performance tuning using deep learning.
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