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How To Choose a Storage Solution for AI Training Data
Selecting the optimal storage solution for AI training data demands careful evaluation. These guidelines will assist in identifying the storage option that maximizes advantages while minimizing limitations.
6 Min Read
Alamy
Data is nothing new. By now, most businesses have developed effective strategies for storing most types of data that power their operations.
AI training data, however, is an exception. Because few organizations began embracing generative AI or developing their own AI models until recently, most lack experience in deciding where and how to store the training data that powers their models.
This inexperience is a critical challenge if you want to take advantage of GenAI. This article will unpack strategies and best practices for storing AI training data.
What Is AI Training Data?
As you likely know if you're familiar with the basics of generative AI technology, AI training data refers to the data used to train the large language models (LLMs) that power GenAI apps and services.
LLMs aim to simulate human decision-making in ways that allow them to generate original content. To understand how humans think, however, LLMs must train on data produced by actual humans (or on "synthetic" data designed to resemble human-generated information). LLMs can't do their jobs effectively unless trained on appropriate data, and the GenAI services they power deliver little value.
The Unique Storage Challenges of AI Training Data
AI training data is not different in a technical sense from other common types of data. It typically includes information such as emails, documents, and possibly audio and video files. That data type is compatible with various modern storage systems, such as databases, file storage, and block storage.
That said, the data that AI models train on is unique in other ways, which can lead to special storage challenges:
Very high data volume: AI training datasets tend to be massive, which means they can consume an enormous volume of storage space and lead to massive storage costs, especially if storage is not cost-optimized.
Irregular data access: AI models typically access training data only when actively training or retraining—events that may happen irregularly. As a result, it can be tough to predict how frequently the data will need to be made available. The unpredictability can affect storage strategies because some storage solutions (like "cold" cloud storage) don't make data readily available.
The complexity of data compression: It's possible in some cases to compress AI training data to save space. However, whether you can compress data and the compression algorithm depends on your model's ability to work with compressed data. For this reason, compression (a bread-and-butter way to reduce storage costs in other contexts) isn't always a reliable option for AI training data.
Changing data: The data used for AI training may change over time. Indeed, keeping data current is vital for ensuring that model behavior reflects the most timely information available. As a result, the ability to update training data is important. However, the feasibility of making changes depends partly on how you store the data. You may also want to version-control the data so you can track how it changes over time, but not all storage systems support this.
For these reasons (and others), there is no simple, one-size-fits-all approach to storing AI training data. The best strategy depends on the type of data you're dealing with, how your models interact with that data, and your business priorities.
Training Data Storage Options
We can't tell you exactly which storage solution is best for your training data. However, we can offer general guidelines about which storage strategies make the most sense under different circumstances.
Cloud object storage
In general, cloud object storage services, like Amazon S3 and Azure Blob Storage, are good options for storing training data when you have a very large volume of data to store because they offer virtually infinite storage capacity. These services also provide built-in versioning, so they are useful if you need to track changes to data over time.
On-premises scale-out storage
On-prem storage built on top of scale-out storage platforms such as Ceph is less scalable than cloud storage in most cases, so it's not ideal if you have truly vast volumes of training data to house. The tradeoff is that this approach may be more cost-effective in the long run than cloud storage since you don't have to pay monthly fees to store your data. Moreover, you don't have to pay egress fees if you move the data outside the cloud.
Databases
Databases are typically not ideal for storing training data because they are less scalable and flexible than other options. With that said, if your training data is structured — if, for instance, you have different categories of data and want to store each separately — a database could be an efficient means of doing that.
File storage
File storage, which houses data inside local file systems, is also usually not a great way to store AI training data. The structure that file systems impose on data can make it challenging to store training data that lacks any coherent structure. In addition, file storage is harder to scale because there's no simple way of extending file systems beyond a single computer or server. (Network-based storage platforms like NFS can enable this, but they are not trivial to set up.)
The exception is situations where you have a relatively small amount of training data to store, and when your model lives on the same machine that hosts the data. In that case, file storage may lead to faster training because data never needs to move over the network.
Conclusion: The Many Ways to Store Training Data
Finding high-performing, cost-effective storage for AI training data is no easy task — and the more data you have, the harder it becomes to store it optimally. The good news is that many types of storage solutions are available. By carefully considering the pros and cons of each one, you can determine which storage option delivers the most benefits with the fewest drawbacks for the training data you need to store.
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