Iryna Kondrashchenko & Oleh Kostromin
DataForce Studio
#1about 1 minute
The hidden complexity of the machine learning lifecycle
Building a machine learning model is simple, but the full lifecycle including data prep, deployment, and monitoring makes production systems very difficult.
#2about 1 minute
Overcoming the fragmented machine learning tool ecosystem
DataForce Studio provides a set of well-integrated components to create a single, unified flow from model building to production monitoring.
#3about 1 minute
Using a model-centric design for a unified workflow
The platform defines a model as a standardized container with rich metadata, allowing all system components to work with it natively without extra configuration.
#4about 1 minute
Ensuring flexibility for diverse model types and use cases
The platform supports everything from traditional machine learning on tabular data to complex large language model pipelines and agent-based workflows.
#5about 1 minute
Avoiding vendor lock-in with an open-source platform
DataForce Studio is open source and uses a core module called Orbits, allowing you to bring your own storage and compute to maintain control over your data.
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