InfoQ Homepage Model Content on InfoQ
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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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Counterfactual Evaluation of Machine Learning Models
Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.
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TensorFlow: Pushing the ML Boundaries
Magnus Hyttsten talks about how Google uses Machine Learning to address problems that were not solvable a year ago, looking at models and how they can be built.
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Deep Learning Applications in Business
Diego Klabjan discusses models, implementations, and challenges developing applications for trading, forecasting, and healthcare, detailing relevant models and issues adopting and deploying them.
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Products and Prototypes with Keras
Micha Gorelick shows how to build a working product with Keras, a high-level deep learning framework, discussing design decisions, and demonstrating how to train and deploy a model.
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Using Bayesian Optimization to Tune Machine Learning Models
Scott Clark introduces Bayesian Global Optimization as an efficient way to optimize ML model parameters, explaining the underlying techniques and comparing it to other standard methods.
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There is No Spotify Model
Marcin Floryan discusses the Spotify engineering culture.
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Beyond Projects - Why Projects Are Wrong and What to Do Instead
Allan Kelly examines the project model and shows why it does not match software development, outlining an alternative to the project model and what companies need to do to achieve it.
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