Data processing
Gathering, storing and modifying data are foundational for the first stage in the AI learning workflow.
Use Nebius AI and Toloka services and tools to process your data before using it for training or inference.
Why Nebius AI is the right choice
Data collection and labeling
Our partner Toloka provides a data-centric environment to support fast and scalable AI development with human insight. It uses crowdsourcing, features fast data iterations, and helps you to scale and achieve optimal quality.
S3-compatible storage
Store, retrieve and manage your data effortlessly with our Object Storage. Optimize data access and durability without compromising on speed.
Marketplace
Enhance your pipeline and modify data with additional third-party tools and products from leading vendors.
Solution architecture
Solution architecture
Prepare your data for supervised, semi-supervised, unsupervised, or reinforcement learning with this set of Nebius AI services and Toloka Data Labeling Platform tools.
FAQ
Why is data processing crucial in AI workflows and machine learning?
Why is data processing crucial in AI workflows and machine learning?
Data processing is fundamental in AI workflows and machine learning because it transforms raw data into usable information. AI algorithms and machine learning models heavily depend on quality data for training and making accurate predictions.
Processing data involves cleaning, transforming and structuring it, ensuring that machine learning models receive accurate inputs, leading to more reliable outcomes.
How does data processing impact the accuracy of AI models?
How does data processing impact the accuracy of AI models?
What are the steps involved in data processing?
What are the steps involved in data processing?