OCI Data Labeling
Oracle Cloud Infrastructure (OCI) Data Labeling is a service for building labeled datasets to more accurately train AI and machine learning models. With OCI Data Labeling, developers and data scientists assemble data, create and browse datasets, and apply labels to data records through user interfaces and public APIs. The labeled datasets can be exported for model development across Oracle’s AI and data science services for a seamless model-building experience.
Data Labeling features
Label different types of data
Upload documents, including PDF and TIFF formats, and add labels. These labels are helpful for scenarios like training custom document-classification models for classifying support tickets and automatically processing refunds based on customer explanations.
Image labeling
Developers can upload raw images, add labels, and highlight areas of images. By adding these labels to images, the resulting datasets can be used to train custom image classification and object-detection models.
Text labeling
Upload text-classification labels and use OCI Data Labeling to automatically identify key information in text. This labeled text can be used to train custom natural-language processing models for information extraction, intent classification, sentiment analysis, and more.
Label and use data easily
Label data faster
OCI Data Labeling provides custom templates and multiple annotation formats. Label data according to the needs of machine learning models. Annotate images, text, or documents in just three steps: Create a dataset by loading data, annotating it, and exporting it.
Export labeled data faster
Export a snapshot of the annotated data record in JSON format to object storage. Access exported labeled datasets across Oracle’s AI and data science services, and integrate them into custom model-building processes without any transformations.
Seamless integration
Use OCI Data Labeling on its own, or access it within other services such as OCI Vision and OCI Language. Developers and data engineers can assemble and label datasets and then easily reference them via OCI AI Services as part of a custom model-training workflow. Data scientists who prefer to build and train their own deep learning or natural language processing models can consume the labeled dataset through OCI Data Science.
Data Labeling use cases
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Image classification
Label datasets consisting of logos, popular clothing silhouettes and colors, types of products, and medical images. Use these labeled datasets for inventory planning, product categorization, shelf management, and medical diagnoses.
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Irregularity detection
Label irregular images to create models that automate elements of product quality checks, defect detection, safety surveillance, and inventory management.
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Forms processing
Label documents to make extracting valuable information easier for patient claims processing, medical report diagnostics, and cellular research.
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Virtual assistance
Classify datasets including receipts, invoices, and POs for customer support chatbots and automated expense filing.
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Information extraction
Tag groupings of words and assign labels. Labeled text datasets can be used for customer survey analysis, topic modeling, and customer support.
Automated Document Classification and Key-Value Extraction Using OCI Document Understanding and OCI Data Labeling
Automating the classification and processing of business documents using AI helps reduce manual effort and errors, especially with a large number of documents of various categories. Learn how you can train custom AI models for document classification and key-value extraction using the OCI Document Understanding and OCI Data Labeling services.
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Start labeling data and make it easier to consume high-quality data in machine learning models.