Azure Open Datasets

Visual Layer is a platform for working with large volumes of image and video data. It supports visual search, filtering, tagging, and dataset structuring across raw files, metadata, and labels. No code is required, and both technical and non-technical teams use it in production. Common applications include curating datasets for machine learning, auditing visual content for compliance, reviewing surveillance material, and preparing media for downstream platforms. The platform detects duplicates, mislabeled items, outliers, and low-quality files to improve data quality before model training or operational decision-making. It is model-agnostic, supports both cloud and on-premise deployment, and is built by the creators of Fastdup, the widely used open-source tool for visual deduplication.

DataChain connects unstructured data in cloud storage with AI models and APIs, enabling instant data insights by leveraging foundational models and API calls to quickly understand your unstructured files in storage. Its Pythonic stack accelerates development tenfold by switching to Python-based data wrangling without SQL data islands. DataChain ensures dataset versioning, guaranteeing traceability and full reproducibility for every dataset to streamline team collaboration and ensure data integrity. It allows you to analyze your data where it lives, keeping raw data in storage (S3, GCP, Azure, or local) while storing metadata in inefficient data warehouses. DataChain offers tools and integrations that are cloud-agnostic for both storage and computing. With DataChain, you can query your unstructured multi-modal data, apply intelligent AI filters to curate data for training and snapshot your unstructured data, the code for data selection, and any stored or computed metadata.