Machine Learning is a set of technologies that uses AI concepts for computational learning and to find patterns. Machine learning has been highly beneficial in areas like customer support, fraud detection, and business intelligence.
Enterprises want to adopt these technologies but they have been out of reach because of the cost of systems, in terms of hardware and software. Even if a business could afford it, they do not have the machine learning talents to design the prediction models or deal with data science.
But now the public cloud service providers have made machine learning services available to everyone at an affordable price. The top cloud providers to enter cloud-based machine learning solutions are Google, AWS, and Azure. From the past 3 years, these companies have made significant investments in AI and Machine learning, from rolling out new services. Google has even been offering the Google Cloud Certification to train students in the related aspects.
There are about 70% of companies that use cloud plans to increase their budget with about 61% of the businesses already having migrated their workloads to the cloud in 2020 [Source: Cloudwards.net]. The Machine Learning services also enhanced the usage of cloud by the enterprises.
Here in this article, we’ll understand what exactly machine learning is, why enterprises want to prioritize machine learning, the machine learning challenges faced by the organizations, and how the cloud solves major machine learning problems.
What is Machine Learning?
- Machine Learning is a part of Artificial Intelligence that focuses on using data and algorithms to imitate the ways of human learning and gradually improve its accuracy.
- The primary aim of machine learning is to allow the computers to learn automatically, without human assistance or intervention, and adjust the actions accordingly.
- The computer learns by observing the data like examples, direct experiences, or instructions, and looking for patterns to make better decisions in the future.
- Machine learning is similar to humans making decisions through past experiences.
Why Enterprises Prioritize Machine Learning?
- Machine Learning is important for enterprises as it helps to understand trends in customer behavior and business operation patterns.
- Based on the predictions of machine learning, organizations have been able to make better and more informed business decisions.
- Many companies have now made machine learning a central part of their operations, including major leaders like Facebook, Google, Uber, etc.
Machine Learning Challenges
Many organizations want to include Machine learning in their work system but they couldn’t because of certain challenges. Detailed description related to this is given below.
- Supply Shortage – As machine learning is still an emerging field, there are not many specialized professionals in this sector. There is a shortage of specialized professionals who have expertise in machine learning and artificial intelligence.
- Huge Deployment Cost – The deployment cost of machine learning is very high. The computational special-purpose hardware requirements add up to huge costs for development, infrastructure, and workforce.
- Difficulty in Scaling Up – It is not easy to scale up in machine learning even with open-source machine learning frameworks like TensorFlow, CNTK, MXNet, etc., because scaling up requires more computers.
Benefits of Machine Learning in Cloud
The cloud providers like Google, AWS, and Azure provide good, great-purpose, and specialized machine learning services. Choosing cloud services in spite of setting Machine Learning systems has many benefits that have been explained below.
Cost Efficiency
Most companies use machine learning as a tool rather than using it on a regular basis. So investing in heavy working and expensive machine learning systems would be too expensive. But the cloud makes machine learning cost-effective.
- The cloud offers a pay-per-use model to use machine learning tools and thus eliminates the issue of investing in expensive machine learning systems.
- The cloud’s pay-per-model would come in handy when the AI and Machine Learning workloads would increase and help companies cut down on costs.
- Machine learning on the cloud also enables cheap data storage.
No Special Expertise
Currently only 28% of companies have experience with AI or Machine Learning. The demand for machine learning is increasing but the IT teams are not skilled enough to implement and support AI and machine learning. However, cloud computing offers some relief.
- With the cloud, companies do not have to worry about having a data science proficient team.
- The artificial intelligence features can be implemented without requiring any deep or hardcore knowledge in artificial intelligence or data science.
- The SDKs and APIs are already provided by the cloud, so that machine learning functionalities can be directly embedded.
Easy to Scale Up
If companies are experimenting with machine learning and its capabilities, then it’s not possible to go full-on in the first go. However, with the cloud, it is easier to scale up.
- Using machine learning on the cloud, companies can first test and deploy smaller projects on the cloud and then scale up as need and demand increase.
- Users can access more sophisticated capabilities without the need to bring in new advanced hardware, with the help of the pay-per-user concept.
Machine Learning Services offered by Cloud
Some of the machine learning services offered by Cloud are mentioned below.
- AutoML – It refers to automated Machine Learning, which is a feature that automatically helps to build the right model.
- Machine Learning Studio – It is all about providing a developer environment where machine learning models and data modeling scenarios can be built.
- Open Source Framework Support – It is an ability to support an existing framework like MXNet, TensorFlow, and Caffe. It usually helps to enable model portability.
AWS, Google Cloud, and Azure are the top machine learning cloud providers and the companies should prefer to choose one from these. While making the choice, companies should make sure that all of their data is stored in a single platform. However, changing the platform won’t affect much as the open-source machine learning framework is supported by all three vendors and can help to change the platforms easily.
