TECHNOLOGY
Why Train AI on Object Storage
Why Train AI on Object Storage
By David Boland
VP, Cloud Strategy
Forget “someday,” our artificial intelligence-powered future is here. Right now, as you read this, thousands of people are using AI–powered solutions to generate images, gather insights, and automate administrative tasks. More software products are introducing AI into their solution offering, and the question of how best to model an AI for your business may soon lay at your feet.
Before any models are built, your organization’s first concern should be data. Though AI models are what garner headlines and draw eyeballs, it’s the data powering them that needs special attention. It’s no secret that an AI is only as good as the data it’s given, and the very best AI need object storage. This article will outline some key factors to consider for using object storage with AI applications.
Scale
The most closely held axiom in AI says that the more data an AI has to train it, the better that AI will be. Large-scale training is in the realm of dozens to hundreds of petabytes, far beyond what can be reasonably accomplished with traditional SAN/NAS storage.
At this scale, object storage becomes the only viable option. Object storage can scale infinitely to accommodate the multi-PB datasets needed to properly seed an AI model. Object storage is also compatible with data at different levels of organization—structured, unstructured, semi-structured—any of which may be the form your AI training data takes.
API
The S3 API is the de facto standard for object storage, and its widespread adoption has made it the de facto standard in the AI/ML data architecture world. Your AI model will need to communicate with its pool of storage—gathering metadata and ingesting information—and the S3 API will facilitate that.
Support for the RESTful API, the object permanence protocol that is backbone of modern AI architecture, is common among object storage services. Other storage types, including NAS, block, and file storage, are limited in the number of APIs they support.
It is important to note that though object storage costs are generally low and consolidating AI training data in object storage can keep costs down, API usage accounts for nearly half of the average object storage bill. These costs can skyrocket, especially when training an AI on an especially large dataset. Be sure to select an object storage provider like Wasabi Hot Cloud Storage that supports the S3 API and does not charge additional data access fees that would drastically impact your budget.
Security & Compliance
With all public storage, the first concern for any data owner is the security of their data. It may contain the personal information of their customers or the proprietary information of a business. It may be data related to a regulated industry such as finance or healthcare and subject to different sets of data storage rules.
It is crucial to store sensitive AI training data in secure, compliant storage locations. Using features like Object Lock for data immutability can further boost the security of training data, making it unable to be altered or deleted until the end of a set retention period.
Of course, there are many more challenges to face when creating an AI-powered application. For a more in-depth guide on traversing them, register for our webinar, Avoiding the Pitfalls of Cloud Storage for Edge and AI-Powered Applications.
the bucket
By David Boland
VP, Cloud Strategy
Forget “someday,” our artificial intelligence-powered future is here. Right now, as you read this, thousands of people are using AI–powered solutions to generate images, gather insights, and automate administrative tasks. More software products are introducing AI into their solution offering, and the question of how best to model an AI for your business may soon lay at your feet.
Before any models are built, your organization’s first concern should be data. Though AI models are what garner headlines and draw eyeballs, it’s the data powering them that needs special attention. It’s no secret that an AI is only as good as the data it’s given, and the very best AI need object storage. This article will outline some key factors to consider for using object storage with AI applications.
Scale
The most closely held axiom in AI says that the more data an AI has to train it, the better that AI will be. Large-scale training is in the realm of dozens to hundreds of petabytes, far beyond what can be reasonably accomplished with traditional SAN/NAS storage.
At this scale, object storage becomes the only viable option. Object storage can scale infinitely to accommodate the multi-PB datasets needed to properly seed an AI model. Object storage is also compatible with data at different levels of organization—structured, unstructured, semi-structured—any of which may be the form your AI training data takes.
API
The S3 API is the de facto standard for object storage, and its widespread adoption has made it the de facto standard in the AI/ML data architecture world. Your AI model will need to communicate with its pool of storage—gathering metadata and ingesting information—and the S3 API will facilitate that.
Support for the RESTful API, the object permanence protocol that is backbone of modern AI architecture, is common among object storage services. Other storage types, including NAS, block, and file storage, are limited in the number of APIs they support.
It is important to note that though object storage costs are generally low and consolidating AI training data in object storage can keep costs down, API usage accounts for nearly half of the average object storage bill. These costs can skyrocket, especially when training an AI on an especially large dataset. Be sure to select an object storage provider like Wasabi Hot Cloud Storage that supports the S3 API and does not charge additional data access fees that would drastically impact your budget.
Security & Compliance
With all public storage, the first concern for any data owner is the security of their data. It may contain the personal information of their customers or the proprietary information of a business. It may be data related to a regulated industry such as finance or healthcare and subject to different sets of data storage rules.
It is crucial to store sensitive AI training data in secure, compliant storage locations. Using features like Object Lock for data immutability can further boost the security of training data, making it unable to be altered or deleted until the end of a set retention period.
Of course, there are many more challenges to face when creating an AI-powered application. For a more in-depth guide on traversing them, register for our webinar, Avoiding the Pitfalls of Cloud Storage for Edge and AI-Powered Applications.