Subject archive for "domino," page 2

Data Science

Providing fine-grained, trusted access to enterprise datasets with Okera and Domino

Domino and Okera - Provide data scientists access to trusted datasets within reproducible and instantly provisioned computational environments.

By David Bloch8 min read

Data Science

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 – including On-demand Spark clusters, enhanced project management, and the ability to export models – that give enterprises unprecedented power to scale their data science capabilities by addressing common struggles.

By Nick Elprin11 min read

Data Science

Announcement: Domino is fully Kubernetes native

Last week we announced that Domino is now fully Kubernetes native.

By Nick Elprin2 min read

Data Science

Natural Language Processing in Python using spaCy: An Introduction

This article provides a brief introduction to natural language using spaCy and related libraries in Python.

By Paco Nathan15 min read

Data Science

Announcing Domino 3.4: Furthering Collaboration with Activity Feed

Our last release, Domino 3.3 saw the addition of two major capabilities: Datasets and Experiment Manager. “Datasets”, a high-performance, revisioned data store offers data scientists the flexibility they need to make use of large data resources when developing models. And “Experiment Manager” acts as a data scientist’s “modern lab notebook” for tracking, organizing, and finding everything tested over the course of their research.

By Domino2 min read

Data Science

Domino 3.3: Datasets and Experiment Manager

Our mission at Domino is to enable organizations to put models at the heart of their business. Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale.

By Domino5 min read

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