How a Data Platform Can Unlock Silos to Accelerate AI Pipelines

An AI strategy is a data strategy, which is why it must deliver seamless data accessibility for it to thrive. In regards to AI models, in particular, accessibility without silos enables data pipelines to flow faster so models can be trained, retrained, and deployed without delays.

The Risk of Data Silos to AI Pipelines

The growth of generative AI highlights a fundamental shift in how quickly technology can scale-and how essential AI has become across industries. This offers enormous opportunities for companies while also placing tremendous pressure on them to innovate faster than ever against competitive pressures.

The journey from AI idea to production is iterative and filled with critical questions. The best responses come with a flexible approach and are continually evolving. It's not just about building a model anymore. Enterprises must think about infrastructure, data security, and scaling to face new challenges-especially in data management, it is a data ecosystem.

As you tap into the power of AI, data preparation is one of the most critical steps to ensure smooth and efficient AI operations. However, the actual modeling is only a small part of the pipeline. Most of the work is data operations, which includes organizing, cleaning, and managing data.

The Challenge and Necessity of Unifying Data Silos

Managing unstructured data across multiple silos is a complex set of tasks. Data silos exist for various reasons, including organizational inefficiencies, the use of outdated technologies, and the growing complexity of AI systems. And yet, data silos, whether on premises or in the cloud, also create new inefficiencies that impede AI pipelines. Consolidating these silos is key to unlocking the full potential of AI.

The problem is that each data silo often has unique storage, processing, and retrieval requirements. For example, data warehouses are typically designed for structured, batch data, while data lakes handle unstructured, scale-out data. As AI use cases expand, the infrastructure that supports these different systems often becomes fragmented.

Storage plays a crucial role in how data is ingested, processed, and used to create accurate, relevant responses in AI-powered applications. A single platform for data set access can enable data to flow seamlessly between different AI pipeline stages-from data ingestion to production. When you unify your data silos, you'll improve data accessibility and drive faster and more efficient AI workflows.

Don't Forget Databases for AI: They're Changing

As AI applications become more sophisticated, databases also need to evolve to handle increased performance and scalability demands. Traditional databases, built around structured queries, are being supplemented by vector databases, which support advanced AI functionalities like vector search. Vector databases use mathematical relationships to establish meaning and context, offering more intuitive and accurate search results for large data sets.

In addition, databases need to accommodate large language models (LLMs) and retrieval-augmented generation (RAG), both of which require massive data sets to train AI systems. As more AI models are integrated into business operations, the capacity and performance planning for these databases becomes a major consideration. Vector databases take up on average 10 times more space than their traditional relational counterparts after sharding. It's crucial to understand the nature of the data being sharded and how storage will manage any resulting data bloat. Optimizing scale-out, speed, and parallel processing with GPUs can significantly enhance cost efficiency when paired with the right solution.

Meeting the Storage Demands of AI Pipelines

When strategizing about AI pipelines, remember that storage demands go beyond just model training. In fact, much of the heavy lifting comes from data preparation, which includes tasks like cleaning, copying, and organizing large data sets. Surveys consistently show that data scientists spend about 80% of their working hours cleaning and organizing data before analysis can begin. In addition to resource costs, these data preparation steps can become a bottleneck if not managed properly. Focus on solutions that simplify this complexity and you'll save time spent on data preparation, enabling you to move from proof of concept to production more quickly.

As data volumes grow or AI models become more complex, FlashBlade^®, a high-performance storage platform, can easily expand to meet new demands without requiring a full system replacement or major downtime. This ensures long-term value in supporting future innovation without unnecessary overhead.

One of the standout features of FlashBlade is its ability to deliver high performance across all stages of the AI pipeline, from data ingestion and preparation to experimentation and production. Unlike traditional storage solutions that might struggle with diverse workloads, FlashBlade provides consistent performance and rapid access to data regardless of the application or workload. It enables the consolidation of silos by unifying file and object storage into a single platform, simplifying management and eliminating the need for multiple fragmented systems. Plus, FlashBlade caters to both structured and unstructured data, providing a seamless interface for businesses to handle complex AI workloads.

As you consolidate data silos, you'll become more efficient and make it easier to access data for AI-driven decision-making.

AI Data Platform: A Future-proof Foundation for AI

AI pipelines rely on efficient data operations. From cleaning and preparing data to leveraging new database capabilities like vector search, the journey to successful AI implementation is filled with challenges that require robust storage solutions. By focusing on unifying data silos and leveraging modern storage platforms, you can unlock the full potential of AI.

Investing in the right infrastructure is not just about meeting today's AI demands-it's about building a foundation that can scale as AI models and applications evolve. FlashBlade is designed to offer long-term value by ensuring seamless scalability, high performance, and simplified data management.

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