Intelligent Mass Storage for AI-Driven Innovation

As AI workflows generate ever-larger datasets, the need for a scalable, intelligent storage solutions is more urgent than ever. Versity’s ScoutAM platform offers a seamless, future-proof solution that spans the entire AI lifecycle—from acquisition and deployment to long term preservation. With cutting-edge features and an energy-efficient design, ScoutAM optimizes performance, and supports sustainable AI innovation.

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Optimizing Scientific Deep Learning AI Workflows

Scientific deep-learning AI workflows generate vast amounts of data, requiring robust storage and archival solutions that balance performance, efficiency, and accessibility. Versity’s integrated data management stack provides a comprehensive solution to this challenge, combining the power of ScoutAM for exabyte-scale data management, ScoutFS for high-performance file and metadata operations, and Versity Gateway for seamless S3 protocol support. This ecosystem enables organizations to efficiently manage their archival data collections.

Cost of Long-Term Data Storage

Archiving large datasets for deep learning models can be costly, especially when considering high-performance storage or cloud storage fees. Through intelligent tiering and integration with low-cost storage media, including tape and cloud storage tiers, ScoutAM optimizes storage costs while ensuring quick access to data as needed.

Explosive
Data Growth

As AI and ML workloads grow, managing massive volumes of data can become unwieldy, leading to slow data retrieval and inefficient scaling. ScoutAM’s modular architecture and seamless integration allow for scalable storage, effortlessly expanding to meet the demands from petabyte to of exabyte-scale datasets.

Access to Archived
Content

Slow data access during model training or evaluation can create bottlenecks, hindering the speed and efficiency of deep learning workflows. With parallel data transfer capabilities and advanced caching, ScoutAM ensures rapid access to critical datasets, enabling uninterrupted training and fast evaluation, even for large-scale models.

Energy and Environmental Impact

AI infrastructures often consume substantial power, and traditional storage solutions contribute heavily to carbon emissions. By using energy-efficient tape storage with disk and object storage systems ScoutAM educes power consumption and carbon footprint, aligning AI workflows with sustainability goals.

Versity-Enabled End-to-End Deep Learning AI Workflow

Managing AI and machine learning workflows involves several critical stages, from data acquisition to model deployment. Versity’s ScoutAM platform is designed to support every step of this process, ensuring that data is efficiently acquired, stored, preprocessed, and accessed for training, evaluation, and deployment. With seamless integration across on-premises, cloud, and hybrid storage environments, ScoutAM helps organizations handle the massive datasets required for deep learning while optimizing performance and cost-efficiency. Below, we detail how ScoutAM supports each phase of the AI workflow, from storage management to model deployment.

Data Acquisition and Storage Management

Deep learning workflows start with acquiring and storing large datasets. Versity’s ecosystem simplifies this process:

ScoutAM serves as the central data management platform, capable of handling exabyte-scale data collections. It integrates seamlessly with on-premises S3 object storage and public cloud services like AWS, Azure, and Google Cloud, providing flexibility in managing diverse datasets.
Versity S3 Gateway bridges the gap between file-based storage systems and S3 object protocols, enabling easy ingestion of S3 data into ScoutAM while ensuring compatibility with existing workflows.

Preprocessing and
Dataset Preparation

Preprocessing involves cleaning, transforming, and organizing data for model training:

ScoutFS, the file system behind ScoutAM, separates metadata (like atime) from actual data, speeding up search and retrieval operations during preprocessing.
ScoutAM’s modular design allows users to add nodes as needed, boosting performance. Caching features also enhance speed by keeping frequently accessed data readily available.

Model Training

Training deep learning models requires fast, reliable access to large datasets:

With Versity Gateway’s integration, users can train models directly on datasets stored in a unified namespace, combining both object and file-based data, eliminating the need for data duplication or transfer.
ScoutAM supports parallel data transfer, ensuring high-throughput data movement across GPU training nodes and preventing slow data access from bottlenecking training processes.

Evaluation and Hyperparameter Tuning

Frequent access to validation datasets is essential during evaluation and tuning:

ScoutAM’s caching mechanism stores recently accessed datasets in fast-access storage tiers, reducing latency during iterative evaluations.
The tiered storage approach moves less frequently accessed data to lower-cost storage (e.g., tape), optimizing resource allocation without sacrificing performance.

Deployment

Deploying trained models often requires integration into production environments:

Versity Gateway enables seamless deployment by allowing applications to access archived datasets using familiar S3 commands, ensuring models can efficiently access both historical and real-time data.
ScoutAM’s orchestration policies automate data movement between storage tiers based on usage patterns, making the solution scalable and cost-effective.

Reduce Costs and Environmental Impact with ScoutAM

AI workloads demand massive amounts of data storage; Versity’s ScoutAM platform helps organizations expand their storage estate by offering a comprehensive set of strategies for data management. By integrating seamlessly with affordable storage technologies—like high-capacity tape storage, object storage, and flexible cloud storage tiers—ScoutAM ensures scalable solutions that grow with your needs. Additionally, ScoutAM supports next-gen technologies like optical glass and ceramic storage. Total cost of ownership (TCO) for Versity customers can be improved by as much as 5 times compared to traditional cloud storage services like Amazon Glacier. This means unlimited data storage with fast local access, fixed and unpredictable fees, and significant savings across the entire data lifecycle.

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Key Benefits

Seamless Integration with Compute Clusters

ScoutAM integrates seamlessly with compute clusters, ensuring that large AI models can access and store data efficiently during training and inference. With the Versity Gateway, ScoutAM bridges the gap between file-based storage systems and S3 protocols, maintaining compatibility with existing workflows and enabling smooth transitions to more advanced data management systems.

High Performance with Efficient Data Access

ScoutAM’s parallel data transfer capabilities and enhanced caching mechanisms ensure fast access to large datasets throughout the AI lifecycle—whether it’s during preprocessing, model training, or deployment. This high-performance access ensures that AI workloads are not bottlenecked by slow data retrieval, enabling efficient and uninterrupted workflow execution.

Scalable Storage Architecture for Growth

The modular architecture of ScoutAM ensures that your storage infrastructure can scale alongside your growing datasets. Whether you’re working with exabyte-scale data or handling increasing volumes of AI training data, ScoutAM adapts by adding storage nodes as needed, ensuring your system evolves with your requirements without compromising on performance.

Ultimate Disaster Recovery

With robust error handling and recovery, ScoutAM safeguards content integrity and ensures uninterrupted access—even during hardware failures or system issues. Its high-availability architecture minimizes downtime, supporting the always-on demands of media production, post-production, and distribution workflows.

Comprehensive and Personalized Support

ScoutAM is backed by comprehensive, personalized support tailored to mission-critical media operations. Users benefit from phenomenal direct access to knowledgeable support teams and a responsive approach to feature requests, ensuring the system evolves with their needs. Versity’s favorable unlimited capacity licensing and zero lock-in model further reduce operational risk and cost. With seamless integration into existing workflows and compatibility with tools like Globus, S3, NFS, and Samba, ScoutAM delivers robust, user-friendly access backed by a support experience media organizations can rely on.

Efficient Data Storage with Less Environmental Impact

Energy consumption is a significant concern for AI infrastructure. Versity’s solution minimizes energy use with intelligent data placement across various storage tiers. For example, archival tape storage uses almost no power when not in use, and object storage tiers are more energy-efficient than high-performance alternatives. Our intelligent caching system ensures that frequently accessed data is stored on high-performance media, while less active data is shifted to low-power alternatives.

Modern Solutions with Personalized Support

With simple-to-use software that requires fewer resources to manage, Versity delivers modern solutions that evolve with your needs. Backed by a strong engineering team and zero lock-in, we’re committed to empowering your storage strategy without the constraints of traditional vendors. Our dynamic roadmap and unlimited capacity licensing provide flexibility, while our independent, single-product focus allows us to prioritize your needs. Versity offers exceptional, direct support and a customer-focused approach, ensuring high satisfaction and quick responses to feature requests.

Future-Proof Your AI Data Storage

As AI and machine learning workloads continue to generate massive volumes of data, having a scalable, cost-effective, and high-performance storage solution is more critical than ever. ScoutAM empowers AI-driven organizations by offering seamless data management, intelligent tiering, and long-term preservation, ensuring teams can focus on innovation without storage limitations. Whether optimizing AI model training, reducing storage costs, or preserving valuable datasets, ScoutAM provides the reliability and efficiency needed to support the growing demands of AI and machine learning applications.

Ready to Revolutionize Your AI Data Strategy?

Connect with us today to get a personalized briefing or live demo of ScoutAM. Experience firsthand how our solutions can transform your data strategy and drive performance.