Is cloud computing good for machine learning?

Machine learning has stepped into the industry for a long time, but it was out of the reach for most enterprises for a while. But as the technology advancement happens, it opens the door for machine learning to be widely available at the enterprise level. This is a revolution as well as disruption of technology. But how does it impact business? Let’s explore it with cloud technology. But before that, let’s do a quick recap on machine learning and cloud computing basics.

What is Machine Learning?

Machine learning is a sub-component of Artificial Intelligence. It can be defined as the algorithms that parse data sets and then learn from them to apply what has been learned to make informed decisions. In the case of Machine learning, the computer program learns from experience by performing some tasks and sees how those tasks’ performance improves with the experience.

It is the state of the art field of AI that is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music services to data security services.

Related post – Top 10 Artificial Intelligence (AI) Trends for 2021

What are Cloud computing and its different models?

Cloud computing is the choice of the hour for most businesses today. One of the reasons cloud computing is booming in the present market scenario is the numerous advantages of cloud computing. According to Gartner’s forecast, the global public cloud service market is expected to reach $247 billion by 2020, about $383 billion. There are different types of cloud computing services models, such as SaaS, PaaS, and IaaS, and choosing the right one for a business is rather challenging.

As most of us know the abbreviations of these three types of major cloud solutions; however, before going into detail, we will explain the terms with a simple analogy.

– SaaS – Software as a service. It is like public transport that has assigned routes, and you, as a passenger, share the ride with other passengers.

– PaaS – Platform as a service. You hire a car with a driver and ask the driver where to go instead of driving it yourself.

– IaaS – Infrastructure as a service. You are not the owner of the car. Instead, you lease the car and drive it. If you want an upgrade, choose a different car.

Hence, each cloud service has its target business needs and audience, and the cloud services are tailored accordingly. So, from a technical perspective, control reduces from IaaS to SaaS. However, as control increases, it requires expertise in the respective field. Cloud services, thus, can be depicted as a pyramid:

What are the challenges to obtaining machine learning capabilities?

Here are the most prominent ones:

1. It requires specialized skill and expertise, which is in short supply in the industry.

2. The deployment cost is high as the computational special-purpose hardware is costly for development, infrastructure, and workforce.

3. Machine learning frameworks often face issues during scaling up as they need more computers.

How is Machine learning with the cloud revolutionizing the business?

There are mainly 4 ways that machine learning is getting benefitted from the intervention of the cloud. They are –

Cost efficiency

As mentioned in the challenges section, machine learning systems use heavy working and expensive systems. However, with the pay-per-use model of the cloud, companies can eliminate this high cost as those machine learning systems won’t be used every day or always. Furthermore, this is true for most enterprises since they use machine learning as a tool. 

With the increase in AI or machine learning workload, the cloud’s pay-per-se model would come in handy and help companies cut down on costs. For machine learning usage, the power of GPUs can be leveraged without investing in cost-heavy equipment. Cloud leverages cheap data storage for machine learning, which further adds up to the cost-efficiency of this system.

No special expertise required

Though machine learning is in high demand, only 28% of companies have experience with machine learning or AI. However, with the increasing demand, the future scope of machine learning is bright. As per the survey, 42% said that their IT team is not skilled enough to implement and support machine learning and AI. This is a crucial gap between knowledge and expertise. There comes the cloud that helps to bridge the gap.

Using the cloud means that companies do not have to worry about infrastructure-related concerns. Even they don’t need to have a data science proficient team. With Google Cloud Platform, Microsoft Azure, and AWS, artificial intelligence features can be implemented without requiring any deep or hardcore knowledge. With the choice of the cloud model, the SDKs and APIs for machine learning functionalities are already provided. Hence, you can directly embed them. 

Easy to scale up

When experimenting with machine learning and its capabilities, it is not wise to go full-fledged at one go. Instead, with the help of the cloud, a company can experiment, test and deploy in small. Then based on the need, they can scale up. Furthermore, with the pay-per-use model, it is easy to access more sophisticated capabilities without the need to bring in new advanced hardware.

Final verdict

To get the benefits of cloud for machine learning, a business needs professionals who are proficient in both technologies so that they can provide maximum value to the organization. Finally, the pertaining question is – SaaS vs. PaaS vs. IaaS: Which Cloud Service Is the best for you? Here the choice totally depends on the business goals. So, first, analyze what your business needs. However, there are some everyday business needs based on which we can choose the appropriate cloud service.