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World Congress 2025 • Aug 20, 2025 • Session details

DataForce Studio

Iryna Kondrashchenko , Oleh Kostromin

Why rely on disconnected tools for your machine learning lifecycle? DataForce Studio eliminates friction with a unified, open-source pipeline spanning from model creation to deployment.

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#1 about 2 min

Navigating the complexities of machine learning model lifecycles

Building production-ready machine learning systems demands rigorous management of data preparation, evaluation, and conceptual drift.

#2 about 1 min

Overcoming ecosystem fragmentation with a unified workflow

A synchronized set of components minimizes deployment friction by replacing highly disconnected machine learning tools.

#3 about 1 min

Implementing model-centric design with standardized metadata containers

Encapsulating model artifacts alongside environmental metadata naturally enables seamless component integration without requiring additional configuration.

#4 about 1 min

Supporting versatile workflows from tabular data to language models

A standardized self-contained format flexibly scales across traditional machine learning setups, large language models, and agent-based workflows.

#5 about 2 min

Maintaining infrastructure control with open source orchestration

The open-source Orbits module acts as an orchestrator that utilizes existing storage and compute infrastructure to prevent vendor lock-in.

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Exploring the big data and machine learning portfolio

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