Data processing
Gathering, storing and modifying data are foundational for the first stage in the AI learning workflow.
Use Compute Power services and tools to process your data before using it for training or inference.
Why Compute Power 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
Prepare your data for supervised, semi-supervised, unsupervised, or reinforcement learning with this set of Nebius AI services and Toloka Data Labeling Platform tools.
Compute Cloud
Virtual machines and
block storage
Managed Service for Kubernetes®
Managed Kubernetes clusters
Object Storage
Scalable data
storage
Getting started with Compute Cloud
Compute Cloud provides the scalable computing power you need to create and manage virtual machines. Create your first VM or instance group.
Getting started with
Object Storage
Compute Power Cloud provides a universal scalable solution for data storage — Object Storage. Create your first bucket.
Concepts overview
Learn more about Compute Cloud concepts and resources.
FAQ
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.
Data processing is essential to the accuracy of AI models. Properly processed and curated data helps in identifying patterns, reducing noise and improving the overall quality of the training data.
AI models trained on clean and relevant data make more reliable predictions and classifications, enhancing their accuracy and reliability in various applications.
Data processing typically involves data collection, data entry, data cleaning, data transformation, data storage, data analysis and data visualization.