At the center of the AI ecosystem lives data. Just as a quality foundation supports every strong physical structure, a quality data pipeline underpins every high-performing AI engine. So, what are AI data pipelines exactly? How are they used? And what makes them different from legacy data infrastructures? Read on to discover the answers to these questions and learn how an AI data pipeline can benefit your organization.
AI is driving the next wave of digital transformation across all industries, opening up doors for organizations to innovate and thrive. With these new opportunities, however, come new challenges.
Traditional data infrastructures weren’t built for the demands of modern AI lifecycles. Their fragmented, multi-system design — requiring frequent internal data transfers — imposes limits on both the volume and speed of data movement. These inefficiencies stifle AI innovation and drain IT budgets, making them a liability in today’s high-stakes environments.
Organizations need to be able to act on more data, faster. Today, purpose-built AI storage is necessary to handle the unique demands of AI data access, and holistic AI data pipelines are necessary to transform raw data into the refined data required for effective AI model training, inferencing, and innovation.
Read on to discover what AI data pipelines are, and why they matter.
Many conversations about AI data management and model training focus on GPU processing needs, but this only represents a small part of the AI data story.
A great deal of heavy lifting occurs before and after the GPU clusters do their work — data reduction, cleaning, fine-tuning, quantization, and retrieval-augmented generation (RAG), among other processes.
Each of the above-mentioned data processing steps are an essential part of a complete AI data pipeline, allowing organizations to transform raw data into robust generative AI models that produce high-quality results.
Without a performance-optimized AI pipeline, organizations risk experiencing slow time-to-market, inadequate model training, under-performing inferencing, and financial losses on their AI investments.
Model builders rely on AI data pipelines to collect, prepare, store, and deliver the data needed to develop and train their AI models. By providing a common platform for all of these phases in the data journey, leading AI pipeline solutions like VAST Data eliminate the time-consuming task of moving data between phases, enabling greater model training predictability and reducing overall training times and costs.
Once an AI model has been trained, it’s time to apply that training to real-world situations. Enterprises within healthcare, financial services, entertainment, and more rely on AI models to make decisions aimed at boosting market competitiveness. Enterprise AI data pipelines help maximize model results with processes such as retrieval-augmented generation (RAG), and feed data back into the model for continuous inference improvement.
Many organizations quickly find AI data processing and management to be far more intensive than they originally thought — and their legacy data systems struggle to keep up. This blog post helps AI teams get ahead of the game by learning how AI gets its data, which processing steps support effective model training, what needs to happen post-training to run accurate inferencing, and more.
With the onset of AI, entire industries are evolving by the day. A delayed model — and a delayed inference — could turn a would-be record-breaking quarter into a budget-breaking bust. Organizations simply can’t afford to rely on slow, outdated data systems for AI innovation.
Facing growing competitive pressure, organizations are needing to move quickly with their AI initiatives. Having one consolidated AI OS simplifies AI pipeline creation, in turn reducing the latency of retrieving large datasets for model training and inference.
Agentic AI systems act proactively and autonomously to achieve a defined set of goals. These complex levels of decision-making require complex machine learning approaches that AI pipelines are uniquely positioned to support.
Data teams are increasingly pushed to do more with less, and managing disjointed systems drives up costs. Add the complexity of deploying a data pipeline on top of this and things get more expensive. However, consolidating onto a unified platform can lower the total cost of ownership and simplify data pipeline deployment.
Learn how Lambda overcame these challenges when selecting VAST Data as the AI data pipeline and platform to underpin its model training infrastructure.
When deciding whether or not to invest in an AI data pipeline, some companies face the following obstacles:
Having amassed deep expertise in their existing data systems, organizations can be reluctant to replace them despite the performance bottlenecks they cause. In these circumstances, a phased approach can be taken over time — such as starting first with AI storage — until the entire data flow is running through an AI-ready pipeline that eliminates the need to move data between siloed systems.
Implementing any new system requires an up-front investment. However, by consolidating multiple data systems into one, and greatly accelerating AI training and inference times, high-performance AI data pipelines such as the VAST AI Operating System lower the total cost of ownership, increase ROI, and improve market competitiveness for organizations.
Organizations can feel uneasy about trusting a new system with their data. However, cutting-edge AI data pipelines are built with cutting-edge security features — multitenancy, encryption, immutable snapshots, and more. VAST, for example, also prioritizes data governance and compliance, giving AI teams full control and transparency throughout the entire data processing lifecycle.
AI models are only as good as the data pipelines that fuel them. Designed to handle massive amounts of structured and unstructured data, AI pipelines are emerging as a key differentiator separating AI leaders from AI followers. Check out this blog post to learn how AI data pipelines transform raw data into intelligent, inference-ready AI models, and why end-to-end data pipelines are critical for AI scalability.
When assessing AI data pipeline options, it’s important to consider these forward-thinking capabilities to meet your AI development needs for decades to come.
Consolidating data storage and processing into one platform eliminates the need for multiple data copies across different environments, reducing redundancy as well as overall system costs.
Fast, flash-based data storage minimizes data retrieval times, accelerates the model training and validation phases, and provides reliable performance even as data volumes and iterations increase.
Performing data preprocessing and transformation operations directly within the storage system reduces the need to move data to separate processing environments and speeds up data preparation.
With one global namespace and high-performance, distributed file and object storage, an AI data pipeline enables seamless scalability of large-scale AI workloads without performance bottlenecks.
An AI data pipeline designed with multi-tenancy provides robust isolation and performance guarantees for teams sharing the same infrastructure, plus efficient resource utilization and data security.
AI pipelines should support the iterative nature of AI development, automatically feeding query analytics back into the model for continuous improvement and refinement based on real-world feedback.
Designed from the ground up to make all data instantly available for AI, VAST is ending the trade-offs of scalability, performance, resiliency and efficiency that have held organizations back from realizing their AI ambitions.
The VAST AI OS’s unified, intelligent architecture delivers all-flash performance and enterprise simplicity for optimal data availability and system speed.
Single-tier infrastructure
NFSoRDMA and GPU-optimized
Massively parallel architecture
Maximize GPU utilization
Acceleration for query engines
VAST’s reliable, enterprise-grade platform supports all of your structured and unstructured data storage needs without sacrificing security.
Multi-tenant
QoS and secure isolation for multiple workloads
Multi-protocol: Unified NFS, SMB, S3, and GPU-optimized
Enterprise reliability and ease of use
Online upgrades and expansions
Zero Trust Security
With VAST’s flash storage technology and compounding data efficiencies, it’s now affordable to make any volume of data AI-ready, on-prem or in the cloud.
Embarrassingly parallel performance and scale
No compromise data reduction technology
All data is AI-ready on affordable flash
Transactional and analytical database services
Integrated metadata indexing
Launch your AI projects smoothly and swiftly with a data pipeline designed specifically to support them. The VAST AI Operating System simplifies pipeline creation and maximizes the quality and speed of model training and inference, helping your organization get ahead.
Schedule a demo with our team today to see how an AI-native data pipeline can address your particular circumstances and challenges.