Every architect has debated the benefits of hosting their machine learning (ML) solutions on-premise or in the cloud. This can be challenging and it can cause a lot of pain that results in poor user experience, ever-increasing costs, etc, and these become apparent when the decision to choose the correct platform to host ML workloads is taken without all the information. However now that you are here don’t have a single concern, for I am here to help you make that decision after conveying all the advantages and disadvantages of both of the approaches.
On-Premise Machine Learning
With this approach, all the technology infrastructure required to run ML workloads is hosted on-site and not hosted by a public cloud provider; in other words, “on-premise” means that the infrastructure is hosted on a company premise. There are many advantages to owning and hosting your machine learning workloads, however, no approach is perfect and it comes with its disadvantages.
Advantages of On-Premise
Cheaper but with Conditions Applied - If you are comparing infrastructure apples to apples it may be cheaper. However, there is a lot of other equipment like power supply, networking equipment, cooling infrastructure, etc. that may not be accounted for during the comparison. For your architecture to be cheaper, there are quite a few considerations:
Improving user experience and delivery of ML results can be expensive, especially with global users. With an on-premise deployment that may not be geographically close to users and machine learning algorithms that are not running on edge locations, the user experience will be impacted. Users further from the on-premise site will see slower results and increased latency. On-premise deployments don’t provide the same access to global infrastructure as cloud providers do and creating a global network to facilitate better user experience can be expensive.
It would be cheaper if we are considering only one deployment. With cloud infrastructure, we are provided the ability to deploy in many different regions and replicate our workload closer to our users for experience. Replicating on-premise infrastructure in a different location doubles our costs and can get expensive.
We aren’t factoring in the additional costs required to manage regular patching of the servers running the machine learning workload and costs associated with securing the on-premise workload.
Ownership of the Underlying Infrastructure - You own all the equipment required to run the machine learning. If ownership is something that is an important factor during your evaluation then it would make sense with on-prem infrastructure. The equipment is also a valid capital expense which means that it has a resale value that we can tap into, unlike cloud costs which are an expense with no resale value. There are many different pricing models available for cloud computing, however, none of the pricing models actually account for outright purchasing the equipment.
Storing larger amounts of data - A large amount of data is usually required to train machine learning algorithms—this data would need to be stored on-premise which can be expensive and cumbersome to manage. It can be very expensive to manage because of the abundant amount of equipment and technology required to store large amounts of data. We also would need to account for the additional manpower required to patch database servers and maintain adequate security for the data stored on-prem.
Expensive overhead investment - Expensive equipment needs to be purchased to facilitate running machine learning workloads on-premise. Equipment used to run ML workloads comes with huge costs and a management burden.
Starting without any foundation - Unless a machine learning algorithm is readily available and fit for the use case, most companies would have to create ML solutions tailored to their specific need. This would mean starting from scratch and investing in areas such as:
Research and development required for machine learning algorithms
Talent acquisition for creating algorithms that can be used for different use cases
A platform to run the machine learning workload that would need to be manually configured. Public cloud services provide resources that are already configured and ready to run your machine learning workloads
AWS Cloud Machine Learning
All the machine learning workloads would be running in the cloud. This would include the computing power required for training and using the machine learning algorithm and the storage of the data required for ML. This means that all the infrastructure would be hosted away from the company premises in a public cloud service provider like AWS.
Check out this link to learn more about ML on AWS!
Advantages of AWS Cloud
No capital expenditure costs - A capital expenditure, or Capex, is money invested by a company to acquire or upgrade fixed, physical or non-consumable assets. There are no capital expenditure costs associated with hosting your machine learning workloads in the cloud. With the pay-for-what-you-use model, we don’t have to worry about purchasing expensive equipment required to run machine learning workloads.
Starting with a foundation - You wouldn’t be starting from scratch, as the underlying infrastructure used to run your ML workloads is already built for you. You can mostly just worry about the machine learning workload itself, because all the configuration is either provided by default or easily configurable.
Disaster Recovery - An added benefit to using public cloud services is that disaster recovery is built into some of the managed services provided on the cloud. Unlike on-premise deployments, we don’t have to build a secondary site and duplicate our infrastructure to ensure some redundancy is built into the infrastructure. This would ensure that in the event of a disaster, it would still be online and running.
Disadvantages of AWS Cloud
No Ownership - You don’t have true ownership of the underlying infrastructure. If that’s an important factor to consider then running ML on the cloud isn’t for you. There are paths to ownership in the cloud like purchasing reserved capacity and dedicated instances/hosts. However, these are just payment models that allow users to rent fixed capacity.
Locked In - With the amount of data required for machine learning workloads, the AWS model of data transfer locks you into using cloud services. Migrating to a different cloud provider can be expensive since data transfer IN costs are free but data transfer costs OUT of AWS have a cost associated with them. This also doesn’t account for the added time and investment required to build out the infrastructure in the alternative cloud provider you are migrating into.
When it comes to comparing costs between on-premises and AWS cloud for ML solutions, the cloud offers several advantages. While on-premises solutions may require significant upfront CAPEX, ongoing operational costs, and scalability challenges, hosting ML solutions on the AWS cloud provides a pay-as-you-go model, flexibility, scalability, and cost optimization options. AWS cloud allows businesses to start small, scale up or down as needed, and only pay for the resources they use, making it a cost-effective choice for many businesses.
However, it's important to note that the cost comparison may vary depending on factors such as the size and complexity of the ML solution, usage patterns, and business requirements. Therefore, it's essential to carefully evaluate the specific needs of your machine learning workloads and then decide on the appropriate hosting solution.