Many enterprises are considering a Private AI solution as companies experiment with how Artificial Intelligence can drive their business forward. Most organizations adopting a multicloud approach are grappling with bringing AI to where their data is generated, stored and utilized, whether on the cloud or at the edge. Moving their data securely across clouds and other sources with full control and ownership is becoming a priority. Private AI is thus emerging as a stronger and safer option as businesses look to embrace new technology and create new workflows. Designed for a specific entity and hosted in a private, protected environment, private AI allows businesses to take full advantage of the benefits of artificial intelligence safely and securely. For highly regulated industries such as Healthcare, Financial Services, and Government, the data privacy and security this offers is paramount.
The benefits of private AI are compelling. Data will not be leaked, sold or used to train a public AI provider. Companies can also move past basic use cases and use both classic and GenAI models to build world-class, enterprise-level AI strategies while maintaining data privacy. It also enables organizations to have full control over their models, as well as how they are monitored and managed. In addition, there’s lower latency and predictable costs at scale. Private AI means that businesses will be ready for anything across the entirety of their global operations.
There are also performance benefits. The infrastructure driving AI must be distributed to balance the requirements of compute-intensive training workloads and latency-sensitive inference workloads. Many companies turn to the public cloud to host their AI workloads. Enterprises that host their models in private environments with proximity to public clouds can not only take advantage of the best of breed AI models from multiple providers, but also avoid the high costs and network latency that they are seeking.
However, it is important to point out that enterprises adopting private AI do not necessarily have to steer away from public services entirely. Instead, they should collaborate with a partner to build a hybrid infrastructure that allows them to get access to all the clouds that matter and minimize the risks. One ideal way to build an AI-ready data architecture that meets the needs of different AI workloads is to work with a cloud-neutral partner like Equinix. If a company chooses a multicloud approach for AI, it can still store data generated outside the cloud, as well as legacy data sources, near the cloud. Equinix supports a multicloud posture allowing for multicloud access on-demand without the potential cost and performance drawbacks.
AI Challenges
Artificial intelligence is a business imperative, but there is work that companies need to do to capitalize on a transition to private AI. First, they need to build an authoritative data core that allows for moving data from the edge to the cloud and back, without ever having to give up control of that data. They also need to build a data architecture that accounts for the unique requirements of distributed AI workloads. Both require companies to think about:
- GPU supply chain constraints
- Limited in-house AI talent
- Data Privacy and Governance
- Infrastructure Flexibility
- Predictable Costs
For companies needing to address the above considerations, teaming up with partners that help them access the infrastructure they need to do private AI right is crucial to success.
Equinix offers a secure and private platform for your Private AI initiatives, enabling you to maximize the potential of your data and models while maintaining confidentiality and adhering to regulatory requirements. At the nexus of data flows, Equinix connects to an ecosystem of more than 10,000 enterprises, including 2,000+ networks and 3,000+ cloud and IT service providers. Platform Equinix also offers global reach and on-demand private interconnection capabilities, meaning enterprises do not have to rely on the public internet to move their most sensitive data into AI models. Sustainability is at the core of this ecosystem, with Equinix’s infrastructure utilizing liquid-to-air cooling through direct-to-chip and in-rack heat exchangers.
Equinix Private AI with NVIDIA DGX provides a turnkey, ready-to-run AI development platform that can bridge on-premises and the cloud with a single-pane view of all resources. Equinix allows businesses to place their NVIDIA DGX systems close to their data with direct high-speed connectivity to the public cloud and an ecosystem of network service providers for pulling information across corporate WAN. This end-to-end managed private AI solution enables enterprises to take advantage of AI infrastructure as a managed service.
The Path to Private AI
As businesses look to harness the power of artificial intelligence, private AI is the key to adapting to the market change. The ability to experiment and innovate without compromising sustainability goals or struggling to gain control of costs is a key asset to enterprises.
This move to AI is not easy under the best of circumstances: Enterprises must consider data security and privacy concerns, and how to integrate with existing systems. As companies continue to scale AI strategies, private AI provides enterprises with the data management and control, cost management and security they require.
Built upon a multicloud foundation, Equinix’s distributed platform architecture brings AI directly to where the data is generated, stored and utilized, while ensuring compliance with data sovereignty and regulatory requirements. Equinix works with leading ecosystem partners to help customers access the infrastructure they need to do private AI properly by offering end-to-end managed services for private AI. Equinix also holds a distinctive position in the digital infrastructure landscape as a vendor-neutral platform, and AI brings an incredible new set of use cases for companies across industries to help build on top of that.
With the support of a partner like Equinix, companies can innovate, unlock strategic IT expertise, and bring their private AI strategies to the forefront. Learn how Equinix is the platform for Private AI here.
· IDC Report: Private AI infrastructure solves for privacy and regulatory compliance requirements
