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Choosing Between Cloud and On-Prem MLOps: What's Best for Your Needs?
There are pros and cons of cloud-based versus on-premises MLOps. Here's how to select the best approach based on scalability, ease of setup, control, and performance needs.
4 Min Read
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As with most modern IT workloads, there is more than one way to go about hosting machine learning operations (MLOps) — meaning the sets of tools and processes that support AI model development and deployment.
One way is to run MLOps tools on-premises. A second way is to use cloud-based machine learning (ML) tools and services.
Which approach is better? The answer, of course, depends on what your needs and priorities are. Keep reading for guidance as we unpack the differences between on-prem and cloud-based MLOps.
What Is MLOps?
Again, MLOps refers to the set of practices that teams use to develop and deploy ML workloads, such as AI models. The purpose of MLOps is to provide access to an integrated set of tools that handle all stages of the ML development life cycle, including:
Design, meaning the process through which engineers devise an overall ML application or model architecture.
Data preparation, which ensures data is in the appropriate format and of the necessary quality to support ML needs.
Development, or the process of building the model or application.
Experimentation, which allows engineers to experiment with tweaks or customizations that can improve ML workload performance.
Deployment, which is when an approved model is placed into production and begins performing inference.
Monitoring, which allows teams to monitor and manage a deployed model to ensure it performs as expected.
Cloud vs. On-Prem MLOps
The tools that support MLOps processes can run on on-prem servers or in the cloud. Both approaches have their pros and cons.
Cloud-based MLOps
Cloud-based MLOps offers two key advantages.
One is that many clouds provide ready-made MLOps pipelines or environments — such as AWS SageMaker and Azure Machine Learning. This means teams can set up MLOps tools without having to deploy, manage, and integrate them themselves. (On balance, we should note that some ready-to-use MLOps platforms, like DataRobot, can run on-prem — so you don't necessarily have to use the cloud if you want a turnkey MLOps solution.)
The other big benefit of cloud MLOps is the availability of virtually unlimited quantities of CPU, memory, and storage resources. Unlike on-prem environments, where resource capacity is limited by the amount of servers available and the resources each one provides, you can always acquire more infrastructure in the cloud. This makes cloud MLOps especially beneficial for ML use cases where resource needs vary widely or are unpredictable.
On-prem MLOps
Running MLOps pipelines on-prem offers some advantages of its own.
For one, you get more control. You can choose exactly which ML tools to use and exactly how to configure and integrate them. The cloud tends to be less flexible because even if you deploy your own MLOps tools instead of using a packaged solution like SageMaker, the cloud may not support all of the tools or configurations you want to use. Some tools may require access to bare-metal hardware, for example, and not all cloud infrastructure supports bare metal access.
On-prem MLOps may also offer better performance. On-prem environments don't require you to share hardware with other customers (which the cloud usually does), so you don't have to worry about "noisy neighbors" slowing down your MLOps pipeline. The ability to move data across fast local network connections can also boost on-prem MLOps performance, as can running workloads directly on bare metal, without a hypervisor layer reducing the amount of resources available to your workloads.
Cloud vs. On-Prem MLOps: When to Use Which
So, when is on-prem MLOps better than cloud MLOps, and vice versa?
In general, deploying MLOps pipelines in the cloud makes sense in cases where you need highly scalable infrastructure. Cloud MLOps may also be attractive for teams that are new to ML tools and want the simplest setup and deployment experience.
On the other hand, if control and performance are your top priorities, consider on-prem MLOps, which gives you maximum ability to do what you want, how you want.
A Hybrid Approach to MLOps
A final note: On-prem MLOps and cloud MLOps don't have to be mutually exclusive. You can set up a hybrid MLOps pipeline in which some components run on-prem and others in the cloud.
For instance, you could design and build an AI model on-prem but train it in the cloud. This approach would allow you to take advantage of cloud infrastructure for the CPU-intensive task of training, while also giving you the flexibility to use tools of your choice for model development.
You could also go on, under a hybrid MLOps approach, to deploy your model either on-prem or in the cloud depending on factors like how many resources inference will require. If inference resource needs fluctuate a lot, cloud deployment may be preferable because it allows you to access more resources when needed, without paying for unnecessary server capacity during times of lower demand.
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