Data Science Blog | Model Management (5)

Model Deployment Powered by Kubernetes

In this article we explain how we’re using Kubernetes to enable data scientists to deploy predictive models as production-grade APIs. Background ...

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Sampling Based Methods for Class Imbalance in Datasets

Imagine you are a medical professional who is training a classifier to detect whether an individual has an extremely rare disease. You train your...

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The Cost of Doing Data Science on Laptops

At the heart of the data science process are the resource intensive tasks of modeling and validation. During these tasks, data scientists will try...

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Benchmarking Predictive Models

It's been said that debugging is harder than programming. If we, as data scientists, are developing models ("programming") at the limits of our...

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Data Science on AWS: Benefits and Common Pitfalls

More than two years ago, we wrote about the misguided fear of the cloud among many enterprise companies. How quickly things change! Today, every...

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Principles of Collaboration in Data Science

Data science is no longer a specialization of a single person or small group. It is now a key source of competitive advantage, and as a result, the...

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Fitting Gaussian Process Models in Python

Written by Chris Fonnesbeck, Assistant Professor of Biostatistics, Vanderbilt University Medical Center. A common applied statistics task involves...

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Introducing the Data Science Maturity Model

Many organizations have been underwhelmed by the return on their investment in data science. This is due to a narrow focus on tools, rather than a...

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Model-Based Machine Learning and Probabilistic Programming in RStan

In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem,...

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Providing Digital Provenance: from Modeling through Production

At last week's useR! R User conference, I spoke on digital provenance, the importance of reproducible research, and how Domino has solved many of the...

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Better Knowledge Management for Data Science Teams

We’re excited to announce a set of big new features that make it easier for you to find and reuse past data science work in your team and...

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“Unit testing” for data science

An interesting topic we often hear data science organizations talk about is “unit testing.” It’s a longstanding best practice for building software,...

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A/B Testing with Hierarchical Models in Python

In this post, I discuss a method for A/B testing using Beta-Binomial Hierarchical models to correct for a common pitfall when testing multiple...

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Faster model tuning and experimentation

Domino provides a great way to iterate on analytical models by letting you run many experiments in parallel on powerful hardware and automatically...

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Topic modeling in 9/11 news articles

This is a guest post by Dan Morris. The interactive dashboard and the code are also available. This post describes a project to visualize topics in...

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A Guide to Machine Learning Model Deployment