Organisations are reshaping their digital strategies, and AI is at the heart of these changes, with many projects now ready to run in production. Enterprises often start these AI projects on the public cloud because of the ability to minimise the hardware burden. However, as initiatives scale, organisations often look to migrate the workloads on-prem for reasons including costs, digital sovereignty or compliance requirements. Running AI on your own infrastructure comes with clear benefits, but it also raises some major challenges that infrastructure and MLOps experts need to consider.
MLOps acts as the enabler in running AI workloads in a repeatable and reproducible manner. MLOps platforms such as Charmed Kubeflow are cloud-native applications that run on Kubernetes. Building such an architecture on-prem helps organisations to easily deploy, manage and scale their AI applications.
Advantages of AI on-prem
When building their AI strategies, organisations should consider factors such as cost-effectiveness, ability to manage, security and compliance, and performance. Let’s take a look at how running AI projects on-prem addresses these priorities
AI on existing infrastructure
Building a completely new data centre for AI projects can be overwhelming and take time, but it isn’t always necessary. If you already have existing infrastructure that you aren’t fully utilising, it could be suitable for your AI initiatives. Doing AI on-prem on existing infrastructure is a great way to quickly kickstart new projects and experiments, assess the possible return on investment of different use cases, and gain additional value from your existing hardware.
Secure ML workloads on-prem
Many organisations have already well-defined internal policies that also need to be followed by any new AI initiatives. Adhering to these policies is easier using on-prem infrastructure, ensuring a secure and compliant foundation for the MLOps platform and enabling you to build repeatable and reproducible ML pipelines. Especially in highly regulated industries, running AI on-prem could accelerate compliance and security check-ups, helping you to focus on building models, rather than security concerns.
While public clouds nowadays offer different types of instances to run machine learning workloads, for enterprises that store all their data on their own infrastructure, moving it would come with a significant cost. You can circumvent this challenge entirely by running your AI projects in the same location that you are already storing your data. This is one of the reasons why organisations often prefer building their AI workloads on-prem
Disadvantages of AI on-prem
Building and scaling AI projects requires computing power. For organisations that need more computing power, this is a big investment to make before even getting started. At the same time, on-prem infrastructure requires a significant upfront cost and comes with the burden of operating the infrastructure post-deployment. On-prem deployments also have only a limited number of pre-trained models and ready-made services that enterprises can take advantage of.
At the opposite end of the spectrum, public clouds are easy to get started and do not require a big investment. They have big libraries of pre-trained models, such as Amazon BedRock, that can give organisations a head-start. That being said, public clouds often prove to be less cost-effective in the long-term.
Rolling out a new strategic initiative such as an artificial intelligence project comes with a new set of challenges. When deciding whether to run your AI initiatives on-prem, there are a number of key factors you should consider to determine whether it’s the right approach for you:
When should you run AI on-prem?
- Compute performance: It’s no secret that AI projects require significant computing power, and these requirements are only increasing. You should only commit to an on-prem AI strategy if you are certain that you have the resources to satisfy these compute demands, with room to scale.
- Industry regulations: Complying with industry regulations is often easier when you have full control over your data on your own hardware. If you operate in highly-regulated sectors such as healthcare or financial services, then on-prem AI is likely to be the right choice.
- Privacy: These same principles extend to the broader realm of data privacy, which plays an important role in any AI project. On-prem infrastructure represents a compelling option for organisations looking to maximise control over their data and ML models.
- Initial investment: The best infrastructure option will depend largely on the budget allocated for the initial investment. If you lack the resources to support upfront hardware costs, public cloud may be more suitable – unless you have existing, unutilised on-prem infrastructure that you can take advantage of.
- Customisable solution: Do you want a ready-made solution, or a platform that enables you to customise your AI deployment to suit your specific requirements? If you’re looking for flexibility, on-prem is the clear winner.
Open source solutions for AI on-prem
Open source is at the heart of the AI revolution. There are a growing number of open source solutions that benefit from wide adoption in the machine-learning world. Organisations can build a fully open source MLOps platform on-prem using some of the leading tools available:
- OpenStack: a fully functional cloud platform that ensures smooth integration with leading performance acceleration devices, such as GPUs.
- Kubernetes: can be used as a container orchestration tool.
- Kubeflow: a MLOps platform to develop and deploy machine learning models.
- MLflow: a machine learning platform for model registry.
Open source tools come with plenty of benefits. However, it is important to choose the right versions. To ensure the security of the tooling as well as seamless integration, organisations need official distributions that are suitable for enterprise deployments – such as those delivered by Canonical.
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Hybrid strategy with open source
According to the Cisco 2022 Global Hybrid Cloud Trends Report, 82% of IT decision-makers have adopted a hybrid IT strategy. Correlating this with all the focus that organisations put nowadays on their artificial intelligence strategy, it is easy to notice that many of the new projects will run on a hybrid cloud scenario. Open source tools – like those that Canonical supports and integrates in an end to end solution – , mentioned also before enable organisations to build and scale their AI initiatives on their cloud of choice. Users can It helps them kickstart on a public cloud to minimise the hardware burden and then develop a hybrid cloud strategy that ensures time effectiveness and cost efficiency.
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