Hybrid cloud is a type of deployment architecture in which the storage or compute capabilities are distributed across on-premise and public/private on-cloud hardware. In the AI field, this means having models deployed in both on-premise and cloud locations.
- On-premise hardware are the GPUs, CPUs and storage devices which are managed directly by you. They can be held locally or put in data centers.
- Public on-cloud hardware is when hardware and resource management is completely outsourced to third-party public cloud providers. The most notable are Azure, AWS and Google Cloud, these are all public cloud providers, meaning that the compute infrastructure comes as a fully managed service. Resource usage is generally shared.
- Private on-cloud hardware is similar to public except the hardware is leased exclusively to a single organization. You basically just rent a piece of hardware and have much more control than what public cloud providers offer.
In this article, we will discuss why a combination of on-premise and public/private on-cloud services, also called hybrid cloud architecture, can be very useful. We will be discussing this topic through the lens of AI operations. In general, what you find when creating an enterprise hybrid cloud structure is that you get the benefits of control and customization with the on-premise data and the flexibility of on-cloud.
Increased data compliance and security
One of the major benefits of opting for hybrid cloud architecture is that you still have access to on-premise hardware. This is important for two reasons:
Full Control of Data from third parties
Firstly, it gives you the ability to fully control your data. This can help when trying to protect it from third parties. In very crucial and vital industries, such as healthcare and the public sector, this is very important. If your company operates in cloud environments, the risk is especially great as cloud provider partners can sometimes number in the thousands. This means that you need to take measures to protect your data.
In an article by Spheron, they explain how having a GPU on-premise “provides an added layer of security. Data breaches or exposure concerns are minimized as access to the GPU is restricted within the organization’s infrastructure.” It is always better to know exactly who sees your data, it is much easier to monitor when operating on the data on-premise.
Increased security from data breaches
Secondly, since cloud giants store so much data, this makes them a huge target for malicious operators. Breaches. Cloud has documented some of the recent data breaches, showing how the aura of security and invulnerability the large cloud providers emanate is exaggerated.
In short, operating on sensitive data with GPUs kept on-premise is a very important option to have. Having the choice between on-premise, which can be used for sensitive and other types of data and on-cloud which can be used for training or experimentation is useful.
Guarantee uptime (Failover)
A second benefit is that you get the ability to guarantee that your services will work, whether the on-premises systems are down for maintenance or the on-cloud ones are.
While not all cloud providers are equally reliable, they generally are fairly stable. This scenario is useful when either the cloud provider or the on-premise hardware needs to undergo maintenance. It allows you to always offer your product to customers. This is also a way to prevent losing customers and make your company more trustworthy.
Prevent vendor lock-in
A third benefit is that you will be able to prevent vendor lock-in. By being flexible with where you store your data, you develop a robust data architecture system.
If on the other hand, you only chose to use one single vendor, you are limited to that vendor’s choices and decisions. Especially in the realm of public LLM APIs and services. This can have a serious impact on your business and organization if the vendor decides to go in a direction that is not agreeable or suitable to you. If that happens, you need to go through the painful process of switching providers and modifying your existing architecture. This is why we think a hybrid cloud architecture is a good option for companies.
Prevent performance bottlenecks
In the case where load balancing is too difficult or the influx of requests is too large, having a cloud provider to offload is very useful for many applications. This is an important method for companies which expect fast growth or need a reliable way to handle times when the load is too large.
How does it work on UbiOps?
UbiOps has documented extensively how its scaling setting can be configured. With the ‘instance type groups feature’, you can group together several hardware instances and define a priority among them. This feature is specially designed for multi-cloud, and in our case, hybrid setups.
You can basically rank instances using a priority score and UbiOps will automatically determine which one to run the model on based on availability.
It is useful because it allows you to use on-premise resources, which are generally cheaper once installed but also use on-cloud resources, which are dynamic and flexible. It allows you basically to get the best of both worlds, the cheap and on-premise hardware when traffic is low and the flexibility of on-cloud when traffic is high.
Conclusion
In conclusion, there are many benefits to deploying in a hybrid cloud architecture. In this article, we discussed three major ones. Firstly, the added safety that comes with having some on-premise workloads, which can be used for sensitive data. Secondly, the feature—which UbiOps offers —is to offload to the cloud if the traffic is too high. And lastly, the ability to guarantee uptime of your service.
Click here if you are interested in Data privacy in Healthcare or why UbiOps is different from other MLOps platforms. If you are interested in a free trial of our product, contact us.
