Machine Learning: Cloud or On-Premise?

machine learning

New technologies are always emerging, changing the way people live and work. Artificial intelligence (AI) is a revolutionary technology impacting virtually every industry. Manufacturing, retail, health care, and consumer electronics are some sectors using AI.

Machine learning (ML) is a significant part of AI, and the two terms are closely related. ML is essentially an application of AI that uses mathematical data models to allow computers to learn with direct human intervention. Companies that use ML reap several benefits, including gaining a competitive edge over competitors slow to adopt this or other AI-powered solutions.

However, deciding if using ML in the cloud or on-premise is better might be challenging. Certain situations may influence an organization’s decision to use platforms for ML workloads.

Here’s more about using it in the cloud versus on-premise and why a company would want to use one setup over the other — or, in some cases, take a hybrid approach.

An Overview of ML

ML is an evolving subset of AI. Its algorithms allow software applications to predict outcomes using historical data accurately. More companies than ever have or are considering investing in various ML-based solutions to benefit their operations.

For example, some large corporations using ML for various purposes include Netflix, Twitter, Meta, Google, and Amazon. Netflix relies on it to curate content recommendations for subscribers. Twitter uses ML algorithms to personalize users’ feeds, and Amazon uses them for retail-oriented tasks like forecasting, data cleansing, and capacity planning.

State-of-the-art ML models require more processing power than a standard computer. Some companies prefer investing in hardware to keep things on-premise, while others choose to use ML in the cloud, which requires them to work with a cloud provider, such as

Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP), or others. ML-based cloud computing services are also referred to as machine learning as a service (MLaaS).

Younger organizations usually invest in new technologies and engage with the latest trends, whereas more established companies might face more tech adoption and implementation challenges. Some older businesses work with a managed service provider (MSP) to assist with IT tasks and services.

Understanding ML in the Cloud

Various cloud providers include ML experimentations in their offerings. For example, AWS has a comprehensive set of AI and ML services. According to its website, over 100,000 clients use AWS services to handle their workloads.

Many businesses have built advanced AI/ML systems on the cloud, but it’s only a matter of time before they realize how expensive it is to pay a provider’s monthly bills. Hosting AI or ML systems with terabytes or petabytes of data is pricey. Still, it can become even more expensive when the provider adds data egress and ingress into the final cost.

Benefits of Cloud-Based ML

Cloud services offer a range of benefits for the average internet user and an experienced data scientist or ML expert.

Here are some of the main benefits of using ML in the cloud:

  • The cloud is highly scalable and secure.
  • It’s useful for anyone looking to train and deploy complex ML or deep learning models.
  • Cloud services allow professionals to access files from anywhere.
  • It eliminates the need to set up the infrastructure for an AI stack.
  • Most cloud providers offer training modules and support services to help clients with troubleshooting.
  • ML enthusiasts enjoy the ease of cloud computing for their applications.

Using the cloud comes with some drawbacks. Companies without skilled staff will face challenges in creating and deploying ML models, regardless of if they use the cloud or an on-premise platform.

Additionally, some organizations might start using too many services from one cloud provider. They might find themselves “locked in” to that provider, making it difficult to transition to a different one in the future.

IT teams might also face obstacles when collecting usable data for cloud ML models. Data comes from disparate sources, and getting it into a functional state is time-consuming for data science professionals.

Understanding On-Premise ML Solutions

On the other hand, some companies, typically those with frequent or extensive ML workloads, might prefer building their own platforms on-premises. However, they would likely need a dedicated IT team with specialized expertise to implement and support them.

Subscribing to a pay-as-you-go basis in the cloud can become expensive. If companies create ML models and deploy them to reach an actual outcome, it can also become CPU or GPU intensive.

Benefits of On-Premise ML

Some organizations benefit from having on-premise computing and storage equipment, which have become more affordable in recent years.

Here are some other benefits of using ML on-premise:

  • Organizations can keep costs down by investing in affordable on-premise computing hardware, often preferred over spending on ever-increasing cloud bills.
  • It enables 24/7 calculation capacity for large ML models or large IT teams.
  • Companies can store and secure sensitive information in their data centers to follow regulatory requirements.
  • On-premise ML can offer lower latency compared to the cloud.
  • ML software and solutions are compatible with on-premise hardware.

A strong business case exists for companies looking to use on-premise ML. The cloud is becoming increasingly popular for all types of applications, but having on-site deployments is still a valuable investment company should consider.

When to Use ML in the Cloud vs. ML on-Premise

C-suite executives, IT managers, data scientists, and ML experts must understand when it’s best to use the cloud or on-premise for these applications. Only then can they decide which is the best investment for their company.

Some key business factors will help organizations decide which option to choose. For example, companies must consider:

One of the biggest drivers of the on-premises ML trend is cost. Companies realize that using cloud services for these applications is becoming too expensive. Organizations with ambitious ML or deep learning goals would do better by investing on-site.

In other words, using the cloud is still effective and offers benefits. However, it’s best suited for organizations that do not prioritize ML workloads, do not expect to invest more in them, or do not see them as an important part of the business.

Hybrid Solutions: The Best of Both Worlds

In addition to on-premise and cloud platforms, companies can also leverage a hybrid ML solution, meaning they use both the cloud and on-premise hardware. This method allows companies to decide which processes will occur on-premise and which will take place in the cloud, offering ultimate flexibility and control over their workloads.

For example, preliminary testing of an ML model can be done on-premises, whereas production-ready models can be developed on a powerful cloud machine. Companies can benefit from using a hybrid solution in many cases and with the right tools.

Some experts suggest that choosing a hybrid solution is a company’s best bet. Doing so gives organizations more freedom, flexibility, and choice regarding how they want to create and deploy their ML workloads, whether on the cloud or on-premise.

Using the Right Infrastructure for ML Applications

The ML market will grow in the next decade and become an increasingly valuable business asset. Using the cloud for innovation seems like a no-brainer, but there’s still value in choosing an on-premise solution.

Companies can contact various cloud service providers and tech vendors to learn more about each option and decide which of the two is right for their business. Every company is different, and there is no one-size-fits-all solution.

Choosing a hybrid solution may be a good option for companies transitioning to a cloud-first model but want to use on-premise hardware for some projects. The decision-makers in a company should speak with IT professionals and research all three approaches to make an informed choice that would best benefit their business.

Ultimately, the choice between cloud-based and on-premise ML solutions hinges on various factors, including cost considerations, data security requirements, scalability needs, and the existing IT infrastructure and expertise within the organization. By carefully evaluating these factors and considering the potential benefits of hybrid approaches and machine APIs, companies can develop robust strategies for leveraging ML to drive innovation and achieve their business objectives.