The business value that AI offers is clear, and enterprises are scrambling to capture that value before the competition. To do that, they’ll have to rethink their IT infrastructure. The conventional compute and storage solutions they’ve relied on up until now simply aren’t up to the task of supporting AI.
A successful enterprise AI strategy requires a data architecture built specifically for AI. This architecture must be able to support different AI workloads, including training workloads that require massive compute capacity and GPU availability and inference workloads that require ultra-low latency. Incorporating cloud adjacent storage practices into your data architecture can help you get the flexibility and performance needed to support AI workloads without sacrificing privacy and control over your data.
How to do cloud adjacent storage the right way
Cloud adjacent storage means placing your distributed, interconnected storage environment in proximity to multiple cloud providers in locations around the world. This allows you to take advantage of cloud services on demand, without the costs and limited flexibility that could come from using cloud-native storage services.
Despite the clear benefits, cloud adjacent storage is not a one-size-fits-all strategy, especially when it comes to your AI use cases. Read on to learn five steps you can take to get the best results from cloud adjacent storage for AI.
1. Assess your data requirements
If you don’t understand exactly what your storage requirements are, then how can you expect to meet those requirements? That’s why building your AI storage environment must start with a comprehensive assessment of your data needs, including:
- How much data you’ll need to store
- How frequently you’ll need to access your different datasets
- Where you’ll need to store your datasets in order to keep latency low or meet data sovereignty and residency requirements
It’s also important to consider the specific requirements of your AI workloads. The need for low latency is a given, but the question is, exactly how low? And where? Once you’ve put specific values to your latency and geographic requirements, you’ll be able to make informed decisions about where you need to host storage in relation to data sources and processing locations.
2. Plan for seamless integration
When you deploy cloud adjacent storage, you’ll need to connect your storage infrastructure to various cloud providers, but also to your infrastructure on-premises and in edge locations. This means that it’s essential to integrate it into your existing hybrid infrastructure while causing as little disruption as possible.
Cloud adjacent storage for AI only works as intended if data can move freely between all your different environments. You’ll need to think about how you can properly configure your network to keep data moving from point to point while avoiding bottlenecks that could exacerbate latency and therefore limit the effectiveness of your AI models. Using a virtual networking solution can help, allowing you to quickly set or reconfigure connections any time the need arises.
It’s also important to ensure you’re only storing the data you really need to store—especially at a time when AI data seems likely to continue growing exponentially. Incorporating data compression and deduplication capabilities into your cloud adjacent storage environment can help you reduce your storage burden, and thus prevent your AI infrastructure from being overwhelmed.
Finally, it’s helpful to work with storage vendors and digital infrastructure providers that have a proven record of interoperability. This will enable a smooth transition; in many cases, you’ll be able to continue using your existing systems from other partners, rather than throwing them out and rebuilding from scratch.
3. Ensure security and compliance
When you feed your data into publicly available AI models, you risk losing ownership over that data. Your data gets trained into the model, meaning that the valuable business insights the data contains could be exposed to anyone who uses the model in the future—including your competitors.
Cloud adjacent storage supports the goal of private AI by giving you a private, single-tenant environment to store your AI data. From there, you can feed it into AI models without it ever leaving your control. Crucially, you can retain ownership over your AI data even if you incorporate public cloud services to help meet your AI infrastructure requirements.
To deliver on the promise of a secure, private approach to AI, you need to work with a storage vendor that offers industry-leading security capabilities, including:
- Robust encryption for data at rest
- Secure access control policies
- Regular security audits to ensure your data remains protected over time
- Ransomware and DDoS mitigation capabilities
Also, by using private interconnection to move traffic between your AI data sources, storage infrastructure and processing locations, you can avoid exposing your sensitive data to the vulnerabilities of the public internet.
When taken together, these security capabilities can help you demonstrate that you’ve taken the appropriate steps to keep sensitive data private, as required by regulations in many jurisdictions worldwide.
4. Optimize performance
Your goal in deploying cloud adjacent storage is to find the right locations to reduce latency and accelerate data processing for your AI applications. Storage performance and AI success have a cyclical relationship: You need high-performance storage to support AI use cases, some of which can help you further optimize storage performance. For example, AI algorithms can help you predict which of your datasets will be accessed most frequently, and then automatically move those datasets to the fastest storage tiers.
By definition, cloud adjacent storage means deploying storage near the cloud. However, it’s essential to reduce latency and optimize performance across your entire AI environment—not just to and from the cloud.
This means keeping your storage close and seamlessly connected to your infrastructure for all your AI workloads, regardless of whether that workload is in the cloud, the on-premises core or at the digital edge. You also need to deploy high-performance connectivity between AI data sources and your storage environment. This ensures that as new data is created, your AI models can start ingesting it without delay and use it to drive timely insights.
Finally, your AI environment is always changing, so optimizing performance shouldn’t be a one-time thing. You need to regularly monitor your storage over time and adjust as needed to continue getting the level of performance required for your AI environment.
5. Consider scalability and flexibility
As we look toward the future, the volume of data consumed by AI models will undoubtedly continue to increase. This means that you need a highly scalable, flexible storage solution to keep up with exponential data growth of applications, workloads, and all the infrastructure that ties it together.
In addition to expanding capacity whenever the need arises, your storage solution should also integrate well with new technologies as required. Using cloud services can help you access the innovative capabilities you need to future-proof your AI infrastructure. Placing your storage adjacent to colocation and cloud providers can ensure access to the widest variety of best-of-breed services.
Finally, no future-proofing strategy would be complete without considering sustainability. Working with a digital infrastructure partner that offers the latest data center sustainability innovations can help you deploy AI infrastructure in a clean, efficient manner. For instance, using liquid cooling in data centers helps enable the higher processing density required for demanding workloads such as AI—without having to sacrifice efficiency to get it.
Start planning your cloud adjacent storage deployment today
The right digital infrastructure partner can help you navigate the challenges of deploying AI-ready storage. With its global reach, flexible digital services and robust partner ecosystem, Equinix is well positioned to be that partner for you. To enable cloud adjacent storage, customers can choose from colocation services in Equinix IBX® data centers or Equinix Metal®, our single-tenant digital infrastructure solution.
With Equinix Metal, you can deploy infrastructure with cloud-like flexibility in the cloud adjacent locations that matter to your business, and easily scale up capacity as your AI approach evolves. In addition, Equinix Metal offers integration with leading storage providers, which can help you get the performance and security capabilities you need to be successful with AI.
To learn more about Equinix Metal for cloud adjacent storage or to get started, visit us today.
When you deploy cloud adjacent storage at Equinix, you also get access to our partner ecosystem, which includes leading AI infrastructure companies like NVIDIA. Read the white paper Enable AI at Scale With NVIDIA and Equinix for more information.
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