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.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
04:25 MIN
Architecture of a unified data and GenAI platform
Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow
05:29 MIN
Extending AI platforms for custom business solutions
Architecting the Future: Leveraging AI, Cloud, and Data for Business Success
05:39 MIN
Exploring the components of the IBM Data Fabric
Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization
08:07 MIN
Building a self-service data and AI workbench
Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow
22:41 MIN
Introducing the Azure AI platform for end-to-end LLMOps
From Traction to Production: Maturing your LLMOps step by step
16:47 MIN
Using DataWorks as a unified IDE for big data
Alibaba Big Data and Machine Learning Technology
00:20 MIN
The lifecycle for operationalizing AI models in business
Detecting Money Laundering with AI
16:06 MIN
Data silos are the enemy of machine learning
AI beyond the code: Master your organisational AI implementation.
Featured Partners
Related Videos
Azure AI Foundry for Developers: Open Tools, Scalable Agents, Real Impact
Oliver Will
The State of GenAI & Machine Learning in 2025
Alejandro Saucedo
Open Source AI, To Foundation Models and Beyond
Ankit Patel, Matt White, Philipp Schmid, Lucie-Aimée Kaffee & Andreas Blattmann
Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow
Kapil Gupta
The AI-Ready Stack: Rethinking the Engineering Org of the Future
Jan Oberhauser, Mirko Novakovic, Alex Laubscher & Keno Dreßel
Developer Experience, Platform Engineering and AI powered Apps
Ignacio Riesgo & Natale Vinto
Industrializing your Data Science capabilities
Dubravko Dolic & Hüdaverdi Cakir
New AI-Centric SDLC: Rethinking Software Development with Knowledge Graphs
Gregor Schumacher, Sujay Joshy & Marcel Gocke
From learning to earning
Jobs that call for the skills explored in this talk.

Team Lead and Senior Software Engineer with focus on AI
Dynatrace
Linz, Austria
Senior
Java
Team Leadership



Data Scientist- Python/MLflow-NLP/MLOps/Generative AI
ITech Consult AG
Azure
Python
PyTorch
TensorFlow
Machine Learning



Data Engineer Platform & Integration DAI Foundations
Fiege Gruppe
ETL
Data analysis
Continuous Integration

