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AI Data Pipelines

AI data pipelines are a process and supporting technology to curate data from multiple sources, prepare the data for proper ingestion, and then mobilize the data to the destination. Data pipelines for unstructured data have special considerations since unstructured data is large, diverse, and difficult to search, organize and move.

AI Data Pipeline Requirements for Unstructured Data

workflows-icon-circleIT organizations need streamlined, automated ways to find and tag data for classification and search and deliver the right datasets to the right tools. AI data pipelines also must include methods to ensure data security and governance. A global file index that can look across all storage facilitates search and curation of unstructured data for AI, including metadata enrichment.

AI data pipelines can also detect sensitive data and move it into secure storage where it cannot be discovered nor ingested into an AI tool. Most organizations have PII (Personal Identifying Information), IP and other sensitive data inadvertently stored in places where it should not live.

Data pipelines can also be configured to move data based on its profile, age, query, or tag into secondary storage—such as cloud object storage where it is significantly cheaper to host and where researchers and data scientists can access it natively for use in cloud-based AI services.

Since unstructured data often lives across storage silos in the enterprise, it’s important to have a plan and a process to manage this data for storage efficiencies, AI, data protection and compliance. Data pipelines aided by a global file index and metadata tagging can help with all these needs.

You’ll need various capabilities, many of which are part of an unstructured data management solution. For example, metadata tagging and enrichment – which can be augmented using AI tools – allows data owners to add context and structure to unstructured data so that it can be easily discovered and segmented.

Read the interview on Blocks & Files: AI Data Pipelines Could Use a Hand from Our Features, Says Komprise

Komprise Smart Data Workflows for AI Data Pipelines

Komprise Smart Data Workflow Manager, included in the Komprise Intelligent Data Management Platform, is a simple UI that allows users without specialized experience to set up, schedule and monitor workflows, including connecting via API to third-party AI services.

Komprise-Duquesne-Data-Intelligence-Blog-THUMBDuquesne University used Komprise Smart Data Workflow Manager to create an AI data pipeline for rapid image search across millions of files in its digital archives. The workflow sent images to AWS Rekognition which analyzed file contents to find specific images needed for marketing campaigns, which Komprise then tagged for future search. The process reduced a 300-plus manual hour effort to less than two hours and demonstrated a repeatable use case for other departments.

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