How AI/ML technologies are fueling cloud adoption

Have you ever got a thought why you see ads or buying suggestions of cloths or earpods or maybe a smartphone that you add into your Wishlist while your colleague, friend, or neighbor getting something else? We know that every individual is different but how these computing devices know that you like cricket or badminton or maybe football, but it also knows what you don’t like. Does it mean computers got the intelligence of the human brain and understands your behavior? Though it is correct partially. With the advancement in modern technology, our devices are getting smarter and learning something new about us to make our life better and easier.

To make this happen small computing minds need someplace to store the data for learning (machine learning) and enhance their intelligence (artificial intelligence). Like every technology, machine learning and artificial intelligence also have some limitations.

If you are working on it, you might be aware of such issues.

1. First, you need data only then you can use it. Everybody knows that artificial intelligence and machine learning needs information to learn more about the world. Still, we overlook this factor in how much information is required. Machines are smart, but they need not just more data than the human mind, but it needs a hundred thousand times more than that. It is not the only issue with the data. The collection of data is also a hard task to accomplish and its compilation to feed your machine.
2. If you somehow manage to complete the stage one of data collections and compilation to fulfill the necessity. Now, you need the computational power in terms of highly clocked GPUs and CPUs to process that huge stack of data. As we all know, great power comes at a greater price. Moreover, you need to pay for an unscalable machine, and you don’t even know how long you will need this.
3. You might complete the first two parts of the problem by spending a huge amount of money and investing a lot of time. Finally, you arrive at the last stage. Here is the best part you don’t have to do much. It will be a test of your patience. In general, machines take more than a day to process the data; sometimes, they went up to a week. It can cause a delay in the release of your software.

Solution

Since every problem has a solution, this also has one “Cloud Computing.” Cloud is scalable technology with pay as per use model that is quite suitable for the Artificial Intelligence and Machine learning models. Cloud machine learning capabilities also help enterprises and make the shift easy when their experiment scales up and goes into production, and demand increases.

You can access the intelligent AI and machine learning capabilities of the cloud without requiring advanced skills in these technologies.

Many public cloud vendor also offers many Machine Learning options that don’t require in-depth knowledge of AI, ML theory, or algorithms. Some cloud vendors even provide tailor-made data for the project.

Conclusion

Every major public cloud vendor offers brilliant general-purpose and specialized artificial intelligence and machine learning services especially they target the startups with their savings plans and services to promote innovation in the industry. You will probably want to priorities the platform that you’ve already selected for your cloud services. However, you can avoid such vendor lock-in when you are using a general-purpose service with an open-source machine learning framework that can work with your cloud vendors. In the current situation, TensorFlow is the only framework that supports the greatest number of cloud platforms. Since technology is getting better with every passing day so we can expect more cross-platform support for frameworks soon.

Author Bio

Cloud Evangelists are Cloud Management Insider in house ambassador for the entire cloud ecosystem. They are liable for propagating the doctrine of cloud computing and help community members make informed decisions.