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Perspective

On Collaboration Between Data Science, Product, and Engineering Teams

By Ann Spencer

Data Science

Model interpretability with TCAV (Testing with Concept Activation Vectors)

By Domino

Code

SHAP and LIME Python libraries: Part 2 - using SHAP and LIME

By Josh Poduska

Code

SHAP and LIME Python Libraries: Part 1 - Great Explainers, with Pros and Cons to Both

By Josh Poduska

Data Science

Trust in LIME: Yes, No, Maybe So? 

By Ann Spencer

Data Science

Learn from the Reproducibility Crisis in Science

By Domino

Data Science

Feature engineering: a framework and techniques 

By Ann Spencer

Data Science

Model Management and the Era of the Model-Driven Business

By Nick Elprin

Data Science

Model Evaluation

By Domino

Data Science

On Ingesting Kate Crawford’s “The Trouble with Bias”

By Domino

Data Science

The Machine Learning Reproducibility Crisis

By Pete Warden

Data Science

Recommender Systems through Collaborative Filtering

By Manojit Nandi

Data Science

Improving Zillow's Zestimate with 36 Lines of Code

By Eduardo Ariño de la Rubia

Data Science

Multicore Data Science with R and Python

By Eduardo Ariño de la Rubia

Data Science

Numenta Anomaly Benchmark: A Benchmark for Streaming Anomaly Detection

By Subutai Ahmad

Data Science

High-performance Computing with Amazon's X1 Instance - Part II

By Eduardo Ariño de la Rubia

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Domino Data Lab empowers the largest AI-driven enterprises to build and operate AI at scale. Domino’s Enterprise AI Platform provides an integrated experience encompassing model development, MLOps, collaboration, and governance. With Domino, global enterprises can develop better medicines, grow more productive crops, develop more competitive products, and more. Founded in 2013, Domino is backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake, and other leading investors.

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