GenAI Requires a New Approach to Data Storage
The new wave of data-hungry machine learning (ML) and generative AI (GenAI)-driven operations and security solutions has increased the urgency for companies to adopt new approaches to data storage. These solutions need access to vast amounts of data for model training and observability. However, to be successful, ML pipelines must use data platforms that offer long-term “hot” data storage – where all data is readily accessible for querying and training runs – at cold storage prices.
Unfortunately, many data platforms are too new zealand whatsapp number data expensive for large-scale data retention. Companies that ingest terabytes of data daily are often forced to quickly move that data into cold storage – or discard it altogether – to reduce costs. This approach has never been ideal, but it’s a situation that’s made all the more problematic in the age of AI because that data can be used for valuable training runs.
This article highlights the urgency of a strategic overhaul of data storage infrastructure for use by large language models (LLMs) and ML. Storage solutions must be at least an order of magnitude less expensive than incumbents without sacrificing scalability or performance. They must also be built to use increasingly popular event-driven, cloud-based architectures.