Subject archive for "models"

Increase cost efficiency for AI and data science

Plug AI’s Silent Drain: How to deliver AI cost-effectively and achieve 722%* ROI

As large enterprises begin to adopt and embrace Large Language Models (LLMs) and seek enterprise-wide AI adoption that transforms every aspect of their business, data science executives are realizing that building a large team and hiring top talent simply isn’t enough to create a broad and bottom-line impact.

By Leila Nouri8 min read


Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration

Data science is facing an overwhelming demand for CPU cycles as scientists try to work with datasets that are growing in complexity faster than Moore’s Law can keep up. Considering the need to iterate and retrain quickly, model complexity has been outpacing available compute resources and CPUs for several years, and the problem is growing quickly. The data science industry will need to embrace parallelization and GPU processing to efficiently utilize increasingly complex datasets.

By Nikolay Manchev10 min read

Data Science

Themes and Conferences per Pacoid, Episode 9

Paco Nathan's latest article features several emerging threads adjacent to model interpretability.

By Paco Nathan29 min read


SHAP and LIME Python Libraries: Part 2 - Using SHAP and LIME

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare to produce model explanations in their own work. If interested in a visual walk-through of this post, then consider attending the webinar.

By Josh Poduska9 min read

Data Science

Data Science vs Engineering: Tension Points

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?” with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points.

By Ann Spencer99 min read

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