Agile AI: Connecting Models to your Data for Maximum Impact
Artificial intelligence is rapidly evolving, and organizations are realizing the critical link between their data and the success of AI initiatives. The focus is shifting from simply building AI models to continuously improving them with real-world data – a concept we call Agile AI. But achieving this requires a basic shift in how we think about data infrastructure and storage.
This article explores the key technologies underpinning Agile AI, the challenges organizations face, and how to optimize storage to unlock the full potential of your AI investments.
The Core challenge: Data Readiness for AI
The biggest hurdle isn’t necessarily the AI models themselves, but rather preparing your data to be consumed by those models. As Fred Lherault, EMEA field CTO at Pure Storage, succinctly puts it: “It’s really about, ‘how do I connect models to my data?’ Which first of all means, ‘Have I done the right level of finding what my data is, curating my data, making it AI ready, and putting it into an architecture where it can be accessed by a model?'”
This means a proactive approach to data management, focusing on:
* Data Finding: Understanding what data you have and where it resides.
* Data Curation: Ensuring data quality, consistency, and relevance.
* AI-Readiness: Transforming data into formats suitable for AI models.
* Accessible Architecture: Building a storage infrastructure that allows models to efficiently access and process data.
The Rise of Inference and Agile Data Management
While model training receives meaningful attention, the inference phase – where models are deployed and used to generate insights – is now the primary focus for most AI customers. Triumphant inference demands agility: the ability to continuously refine models based on new data and feedback.
This agility is powered by a suite of emerging technologies:
* Vector Databases: These databases store data as vector embeddings, enabling semantic search and similarity matching – crucial for RAG.
* RAG (Retrieval-Augmented Generation) Pipelines: RAG combines the power of large language models (LLMs) with access to external knowledge sources, improving accuracy and relevance.
* Co-Pilot Capabilities: AI assistants integrated into workflows, providing real-time support and insights.
* Prompt Caching & Reuse: Storing and reusing frequently asked questions and their corresponding responses to reduce computational load.
Storage Challenges in the Age of agile AI
These technologies introduce unique demands on storage infrastructure. Organizations face two key challenges:
- Connectivity: Seamlessly connecting to RAG data sources and vector databases.
- Scalability: Handling significant and often unpredictable increases in storage capacity.
These challenges are often intertwined.
The Data Amplification Effect of Vector Databases
Vector databases, while powerful, can dramatically increase storage requirements. When data is converted into vector embeddings, it’s often amplified – sometimes by as much as 10x.
Consider this: a terabyte of source data can easily translate into a 10TB vector database. This amplification requires organizations to anticipate and plan for ample storage growth.It’s a new consideration for many as they begin leveraging AI.
Managing Capacity Spikes: Checkpointing and Beyond
Capacity demands aren’t limited to vector databases. Processes like checkpointing – creating snapshots for rollback purposes during AI processing – can also generate massive data volumes.
To address these challenges, a flexible and scalable storage solution is essential.
Pure Storage: Enabling Agile AI with Evergreen-as-a-Service
Pure Storage’s Evergreen-as-a-service model provides the agility needed to rapidly scale storage capacity on demand. Beyond scalability, Pure Storage offers solutions to optimize storage efficiency and performance:
* Key Value Accelerator: This innovative technology stores AI prompts (and their responses) in file or object format, reducing the burden on expensive GPU cache.
* Reduced GPU Load: By caching frequently asked questions, the Key Value Accelerator minimizes redundant computations. If a GPU receives a question that’s already been answered, it can retrieve the response from Pure’s storage instead of recalculating it.
* Performance Gains: Lherault reports response times can improve by up to 20x, notably for complex queries generating thousands of tokens. This translates to faster insights and a more responsive AI experience.
* Cost Optimization: Reducing
