For quite a long time now, machine learning has been out of reach for most enterprises. The hardcore machine learning that adds real value to the organization i.e. However, even as we are speaking, technology is advancing. And this advancement has trickled into the domain of machine learning as well to make it widely and properly available for a variety of enterprises. And if you examine the long-term effects, this is nothing less than disruption and revolution. But, how will businesses actually be affected? Let’s dig a little deeper into it today.
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What is machine learning?
A quick recap for those who know and a quick intro for those who don’t.
Machine learning is a subset/ part of the entire, vast field of artificial intelligence. It is concerned with the development of self-learning algorithms. These algorithms are trained through labeled or unlabeled data sets and examples, then employed to make predictions against new patterns of data.
As one can guess, machine learning was and is a huge leap in the realm of artificial intelligence. Instead of using static programs to make decisions, the data presented to the algorithm at that moment is used to make decisions. This is similar to how humans make decisions. Have an inkling of what you are looking for through past experiences (the ‘training data’ in case of the algorithm) and using that plus the data at the moment, arrive at a decision.
Although a lot of developments have been made, a lot of work is still left to be done. Scientists and researchers envisage a future where no human intervention and additional programming will be needed for the algorithm to arrive at an answer.
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Challenges to the entry of machine learning capabilities
Here are the biggest ones:
- The specialized skill and expertise required which is in short supply and not easily available
- Deployment costs. The computational special-purpose hardware requirements add up to greater costs for development, infrastructure, and workforce.
- Even with open-source machine learning frameworks like CNTK, MXNet, and TensorFlow, run into problems when scaling up due to the requirement of more computers.
How machine learning in the cloud will revolutionize businesses
There are 4 major ways in which machine learning in the cloud will act as a boon for businesses. These are:
The cloud has a pay-per-use model. This eliminates the need for companies to invest in heavy working and expensive machine learning systems that they won’t be using always and every day. And for most of the enterprises, this is true since they use machine learning as a tool and not as the modus operandi.
When AI or machine learning workloads would increase, the cloud’s pay-per-se model would come in handy and help companies cut down on costs. The power of GPUs can be leveraged without investing in cost-heavy equipment. Machine learning on the cloud enables cheap data storage, further adding to the cost-efficiency of this system.
No special expertise required
According to Tech Pro research, only 28% of companies have experience with AI or machine learning. Demand for machine learning is increasing and the future scope of machine learning is bright. 42% said that their IT team is not skilled enough to implement and support AI and machine learning. This suggests a crucial knowledge and expertise gap. But, the cloud helps in bridging it.
Using the cloud means that companies do not have to worry about having 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. The SDKs and APIs are already provided so machine learning functionalities can be directly embedded.
Easy to scale up
If a company is experimenting with machine learning and its capabilities, it does not make sense to go full-on, full out in the first go only. Using machine learning on the cloud, enterprises can first test and deploy smaller projects on the cloud and then scale up as need and demand increases. The pay-per-use model further makes it easy to access more sophisticated capabilities without the need to bring in new advanced hardware.
How to be a part of this revolution
As businesses take to machine learning and the cloud together, they’ll be needing professionals who are fluent in operating both and can provide maximum value to the organization. Traditional university courses do not provide the curriculum in classrooms to ready eager students for it. But, at upGrad, we provide the best of both worlds- an online, easily accessible platform plus an integrated, classroom environment.
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What are the advantages of the cloud?
With almost all businesses moving to the cloud platform, it is obvious that there are several advantages that this technology has brought forth. Firstly, the cloud offers excellent scalability, which is especially crucial for rapidly expanding businesses. Moving infrastructure to the cloud also helps cut down IT expenses to a great extent. It also offers companies the flexibility and options to collaborate more efficiently and across boundaries; geographical constraints no longer matter. The greatest business advantage is perhaps having uninhibited and all-time access to data and resources from anywhere in the world, which ensures business continuity and prevents impacts on productivity.
How does cloud computing impact our personal lives?
The uses of cloud technology are not only reserved for business and commerce. We also use cloud computing in our daily lives, most often unknowingly. For instance, the music and video streaming apps that you use every day are heavily dependent on cloud storage for providing you with uninterrupted service and an enriched musical experience. Next, the social media platforms also resort to the cloud platform to store your images, comments, videos, and other data. Your smartwatch and other wearable devices also use cloud technology. And if you are a keen online shopper, you must know that all your data and preferences are saved somewhere in the cloud; this helps online shopping apps offer you a personalized shopping experience.
Which cloud computing platforms work best for machine learning?
Today, most businesses engaged in machine learning projects have resorted to shifting to the cloud platform. Some of the best cloud computing platforms for machine learning are Amazon Web Services, Google Cloud, Microsoft Azure, and IBM Cloud, among others. Employing different web services in the cloud designed for machine learning has helped them focus on core business competencies and ensure hassle-free management of machine learning infrastructure.