AI and data are inseparable. The 2022 IBM Global AI Adoption Index noted that most businesses get their data from more than 20-50 data sources, with 5% of respondents topping 1,000. That’s a lot of data! However, data is not useful by itself. You need to do the work of cleansing and preparing it, as well as analyzing it to extract insights. AI is just the tool for the job. Companies deal with massive amounts of data from so many different sources that they need to leverage AI-powered software to make analysis feasible. But running AI software and ML algorithms means you need speedy storage to handle the processing. Depending on your IT infrastructure and the amount of data you’re working with, it can take anywhere from a couple of hours to a couple of days to train a machine learning model. This timespan is significantly impacted by components like processing power, storage latency, and network speeds. Since speed is a primary bottleneck, we’ll consider which storage solutions are ideal for AI.
5 Requirements for AI Storage
Storage options are faster and more compact than ever.It’s worth mentioning again that AI and its subsets (machine learning and deep learning) live on data. For this reason, systems must be scalable, cost-effective, secure, location agnostic, and built for hybrid storage. What does each of these attributes imply for your storage choices?
- Scalability. AI requires large data sets, so storage solutions need to be able to scale accordingly. The Inception V3 model from Google contains a little under 24 million parameters and requires about 1.2 million data points (in that case, labeled images) to be trained for classifying images. Managing data sets at this scale would be impossible without scalable storage solutions. To improve scalability, IT teams must leverage modular storage options they can deploy with ease and flexible storage structures like object-based storage.
- Low latency. Since AI requires so much data processing, speed is the most important characteristic of good storage. AI storage uses a combination of SSD and NVMe flash storage to provide ultra-low latency but dense storage. This combination will ensure that your training efforts don’t bottleneck due to storage speeds.
- Data redundancies. AI applications may be handling petabytes of data which can make traditional backup strategies challenging. Still, data must be protected. One way organizations manage this is by using object storage, which is designed with redundancy built-in. While it’s still recommended to have offsite backups for mission-critical data, the capabilities of object storage can allay many of your data reliability concerns.
- Location agnostic. Teams that work with AI and deep learning often use a combination of on-premise equipment and cloud infrastructure. They choose this approach because remote teams, scalability, and cost make hybrid computing essential to getting work done. Hybrid computing allows businesses to process sensitive data on-premises or that requires lower latency and offload the rest to the cloud. That’s why on-premise hardware should easily integrate with cloud solutions to simplify data flow to and from the cloud.
- Flexible storage. Data sets have differing performance requirements, and storage solutions must be flexible enough to meet the needs of your AI. Instead of a one-size-fits-all approach, teams do their best to customize solutions with hybrid architectures that help them reach project goals. Flexible systems mix storage drive types, allowing teams to optimize storage for the job without missing a beat.