Data Management Glossary
AI Inferencing
AI inferencing is the process of using a trained machine learning (ML) or deep learning model to make predictions or decisions based on new input data. It’s the phase after training, where the model applies what it has learned to real-world scenarios, such as classifying images, transcribing audio, or generating text.
Inferencing happens in environments where speed and efficiency are critical: cloud platforms, edge devices, mobile apps, or enterprise applications. For example:
- A recommendation engine showing content on Netflix
- A chatbot responding to customer questions
- An autonomous car identifying road signs in real time
The Importance of Unstructured Data for AI Inferencing
AI models, especially large language models (LLMs) and multimodal models, rely heavily on unstructured data for both training and inferencing. Examples include:
- Text documents, emails, PDFs (natural language processing)
- Images, videos, medical scans (computer vision)
- Audio recordings, sensor logs (speech and signal processing)
At inference time, these models often consume unstructured inputs, such as:
- A radiology image for diagnostic classification
- A customer service transcript for sentiment analysis
- An enterprise document for summarization or Q&A
Without access to high-quality, context-rich unstructured data, AI inferencing can’t produce relevant, accurate results, especially in enterprise use cases where private data (vs. public web data) is the most valuable.
How Does Komprise Intelligent Data Management Support AI Inferencing?
Komprise specializes in unstructured data management, with capabilities highly relevant to AI inferencing workflows:
- Data Discovery & Indexing: Komprise indexes and classifies unstructured data across heterogeneous data storage systems (on-prem, NAS, cloud), helping organizations find relevant data for inference tasks.
- Metadata-Driven Insights: Komprise builds a metadata catalog of files, or metadatabase, which can be searched and filtered based on content, age, usage, owner, etc. This supports use cases like:
- Finding unstructured datasets for inferencing
- Identifying compliance risks (e.g., inference using PII-containing data). Learn more about Komprise sensitive data detection and PII data.
- Data Preparation for AI Pipelines: While not an ETL tool, Komprise can help curate and organize unstructured data so it’s ready for ingestion into AI pipelines, e.g., by exporting data to object stores (S3) used by inference engines or ML frameworks like Databricks, AWS SageMaker, or Azure ML.
- Intelligent Data Tiering: Komprise enables policy-based movement of cold or inactive data to lower-cost storage, while maintaining access, which is essential for keeping inference pipelines cost-effective. Learn more about Komprise Transparent Move Technology.
AI Inferencing FAQs
How does unstructured data quality affect AI inferencing costs?
AI inferencing costs scale directly with the volume and quality of data processed per query. When inferencing pipelines pull from poorly curated unstructured data stores — files with missing metadata, duplicate content, or irrelevant archive data mixed with active working files — models process more tokens per inference than necessary, driving up compute costs and slowing response times. Komprise reduces inferencing overhead by curating only relevant, high-quality unstructured data into AI pipelines through Komprise Smart Data Workflows, and by keeping active inferencing data on fast, appropriately tiered storage while cold data is moved off primary flash automatically.
How does intelligent tiering affect inferencing latency and throughput?
Inferencing workloads that retrieve data from high-latency storage tiers — cold object storage, tape, or deep archive — introduce pipeline delays that degrade throughput and user experience in production AI systems. Komprise Intelligent Data Management maintains a continuously updated Global Metadatabase that tracks data access patterns across hybrid storage environments, making it possible to keep frequently accessed inferencing data on fast primary or NVMe storage while cold data is tiered automatically. This keeps inferencing pipelines running at full speed without requiring IT teams to manually manage data placement across storage tiers.
How does Komprise support governed AI inferencing for regulated industries?
Regulated industries including healthcare, financial services, and life sciences face strict requirements around what data can be used for AI inferencing, where it can be stored, and who can access it. Komprise addresses this through policy-based Komprise Smart Data Workflows that classify, tag, and route unstructured data based on sensitivity, compliance classification, and business context before it enters an inferencing pipeline. KAPPA data services can extract domain-specific metadata — such as DICOM header fields in healthcare or project codes in financial services — and load it into the Global Metadatabase for governed, auditable search and retrieval. This gives compliance and security teams visibility into exactly what data is being used for AI inferencing and why.