Data Science
Model interpretability with TCAV (Testing with Concept Activation Vectors)
By Domino
6 min readBlog archive, page 24
Code
SHAP and LIME Python libraries: Part 2 - using SHAP and LIME
By Josh Poduska
9 min read
Machine Learning
Creating Multi-language Pipelines with Apache Spark or Avoid Having to Rewrite spaCy into Java
By Holden Karau
5 min read
Data Science
Data Science vs Engineering: Tension Points
By Ann Spencer
99 min read
Code
SHAP and LIME Python Libraries: Part 1 - Great Explainers, with Pros and Cons to Both
By Josh Poduska
6 min read
No Tag
Big data, big problems: Nate Silver of FiveThirtyEight shares tips for navigating today’s data science challenges
By Domino Data Lab
5 min read
No Tag
How Data Scientists Can Avoid Three Common Collaboration Challenges
By Domino Data Lab
6 min read
Perspective
How Your Data Science Team Can Improve Knowledge Management—And Why It Matters
By Domino Data Lab
7 min read
Product Updates
AWS and Domino Data Lab: containerized data science in AWS utilizing Kubernetes
By Domino Data Lab
1 min read
Model Scalability
Josh Poduska on tracking model lineage
By Josh Poduska
7 min read
Perspective
Collaboration Between Data Science and Data Engineering: True or False?
By Domino
32 min read
Perspective
Growing Data Scientists Into Manager Roles
By Ricky Chachra
18 min read
Data Science
Domino 3.0: New Features and User Experiences to Help the World Run on Models
By Akansh Murthy
7 min read
Data Science
Justified Algorithmic Forgiveness?
By Domino
13 min read
Data Science
Trust in LIME: Yes, No, Maybe So?
By Ann Spencer
7 min read
Perspective
Why Models Will Run the World
By Matthew Granade
14 min read