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The Role of Containers on MLOps and Model Production

Container technology has changed the way data science gets done. The original container use case for data science focused on what I call, “environment management”. Configuring...

PyCaret 2.2: Efficient Pipelines for Model Development

Data science is an exciting field, but it can be intimidating to get started, especially for those new to coding.  Even for experienced developers and data...

Why models fail to deliver value and what you can do about it.

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for...

Domino Paves the Way for the Future of Enterprise Data Science with Latest Release

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. We’re introducing groundbreaking new features –...

Evaluating Generative Adversarial Networks (GANs)

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are interested in a tutorial...

Data Drift Detection for Image Classifiers

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production....

Model Interpretability: The Conversation Continues

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from Bin Yu, W....

On Being Model-driven: Metrics and Monitoring

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and...

Understanding Causal Inference

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by...

Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Fenner. The excerpt and...

Manual Feature Engineering

Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine Learning with Python...

Data Ethics: Contesting Truth and Rearranging Power

This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing...

Seeking Reproducibility within Social Science: Search and Discovery

Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search & Discovery” at Rev....

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