Many organizations opt against training their own AI models due to high costs and rapid GPU hardware evolution, making cloud options more appealing. Key challenges in AI application are centered around production, particularly in data curation, inference, and leveraging technologies like retrieval-augmented generation (RAG) and vector databases. During a recent event, Pure Storage executives emphasized the importance of fine-tuning and real-time data integration to extract actionable business insights from AI. The focus is on creating consumer-facing “killer apps” that seamlessly integrate AI with existing systems for improved customer interactions. Pure’s innovative solutions, such as the Key Value Accelerator, enable efficient storage management by reusing AI prompts, drastically reducing response times and costs. This adaptive approach ensures organizations can scale their AI capabilities effectively while leveraging existing infrastructures. Ultimately, companies need agile strategies to effectively connect AI models to their data for long-term success in AI utilization.
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