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An on-premise AI platform is a platform that runs AI services and applications within the organization’s physical environment, rather than being hosted on the cloud. As a result, it is maintained and operated by the organization’s employees, rather than by the external cloud provider.
On-premise AI platforms often allow enhanced security and privacy, as well as more customization and control. Regulated industries are often required to ensure on-premise AI deployment to meet compliance regulations. When choosing an on-premise AI platform approach, it’s important to ensure the organization has the internal resources and knowledge to support such platforms.
An on-premise AI solution offers advantages to Heads of ML, Data Engineers and other data professionals. Among these are security, control and performance benefits. Here’s a detailed deep dive:
Industries that need to prioritize data security, regulatory compliance, and high-performance computing can benefit from on-premises AI deployments. Example industries include:
An on-premise AI platform can be important for general AI applications due to several key reasons:
However, it’s important to note that on-premise AI platforms also come with their own set of challenges and costs, such as the need for significant upfront investment in (potentially scarce) hardware and infrastructure, higher maintenance requirements, and the need for in-house expertise to manage and operate the AI systems. It’s important to find a solution for the automatic orchestration of resource allocation and control, automatic scale up/scale to zero, and team-wide GPU management in order to make the most of this AI investment.
Implementing an on-premise AI platform requires careful thought and planning. Based on our experience working with global companies on operating and deploying these platforms, here are some best practices to follow.
The first step is determining the use case that the platform will serve. This could range from data analysis to automating specific tasks to many more possibilities. Understanding the use case at this stage is critical; on-premises deployment is less flexible for changes and scale than cloud-based solutions. Therefore, understanding what you need to achieve and whether the project will be a classic ML project, a deep learning one, generative AI, or a different type will deeply impact your next steps.
Based on your use case, determine resource procurement needs. To ensure you are choosing the right hardware, here are some example questions to ask:
Ensure hardware is shipped and installed on time for your project’s timelines, since your operations rely on them. There are components that could take even a year to arrive. It’s also recommended to come up with a plan B if components aren’t ready on time.
While your servers are on-prem, your Proof of Concept doesn’t have to be. Use synthetic data and the cloud for more efficient operations, before developing your actual project on-premises. This will help you speed up the process and get to production deployment faster.
Ready to get started with on-premises AI? Book a consultation here.