Building storage infrastructure for machine learning

A major issue with implementing machine learning projects after creation and training into the data-intensive enterprise is that it’s taxing underlying data storage and management infrastructure. Prototypes and training infrastructures are typically built on whatever Enterprise storage the organisation uses – or the team building it decides to roll their own with a white box and a mix of open source, home grown, and commercial tools and applications. An eventually successful machine learning programme, usually runs into a problem… A relatively small planning effort can save a lot of time and money, and it just makes sense to plan for success. The larger the project, the more likely it is to need a storage system that can scale to capacity, along with high-density building blocks to reduce the number of devices and data-centre overhead as the project grows.f scale in the transition from training into production…

CLICK TO READ FULL ARTICLE