Intelligent cloud is the hot new thing. As per their Q1 2019 update,
Amazon Web Services (AWS) revenue jumped 45% year over year to $7.43 billion for the fourth quarter of 2018, Microsoft Azure revenue grew 76%, and Alibaba reported that its cloud unit is nearing $1 billion per quarter
This surge in the cloud provider’s revenue speaks at length about the growing interest of companies in cloud technology. And the pace at which the cloud revolution has stormed the industry can no longer be termed as a trend. Instead, has become a long-standing reality that no business – tech or non-tech – can turn a blind eye to.
At the same time, it wouldn’t be false to say that the rise in cloud adoption didn’t take anyone by surprise. Tech pundits had long forecasted that by 2019-20 cloud adoption will become the lifeblood of every industry.
So whether you should plunge in the cloud revolution is no longer a matter of discussion. Now, the question is – what’s next?
After computing, storage and networking capabilities, what more can cloud offer? And How will it lead the future trends?
The Road Ahead
According to the 2019 tech trend report released by Deloitte,
After cloud and analytics, cognitive technologies such as Machine Learning (ML), Artificial Intelligence (AI), Deep Learning and Natural Language Processing (NLP) will lead the pack, followed by digital reality and blockchain technology.
And that’s not it,
Spending on AI is estimated to reach $57.6B by 2021, increasing from $12B in 2017.
Tech giants like Amazon, Microsoft and Google are bidding large on AI which is allegedly the prime reason behind these rising numbers. They are now focused on making AI accessible to every business, after successfully doing the same with the cloud technology.
And to achieve this, the tech companies are merging the power of the cloud with the intelligence of AI. They are tactfully using the best of both worlds to lead a tech revolution.
Microsoft famously termed this blend of intelligence and cloud as Intelligent Cloud. It refers to the integration of artificial intelligence into cloud applications. The basic of this up and coming technology is to incorporate intelligence into the cloud services so as to expand its functionalities.
Below are the two primary reasons that back up the success of this upcoming trend.
- As the cloud has reached every nook and cranny of the business world i.e., approximately every business is using the cloud platform for one or the other purpose, thus, any updates/advancements in the cloud would directly impact a large base of customers.
- In the case of ML, the larger is the amount of data used for training the system, the better is the resulting intelligence.
Cloud applications collect an enormous amount of structured and unstructured data, as a byproduct of its wide reach. This heap of collected data is a valuable trove of information which when fed to the ML algorithms can result in remarkable results promising precision and accuracy.
Ushering Into the Era of Intelligent Cloud
Stepping into the era of the intelligent cloud has doubled the expectations of the businesses. Gone are the days when they were striving for predictive analytics. Now, companies not only want to predict the future but also to find probable solutions that can handle the predicted future, also known as prescriptive analytics.
This means, in addition to using the data to predict trends, now companies want to utilise the data to find the best course of action for these trends. This is what the intelligence cloud is striving to achieve. To train the cloud application into not only providing analytics but also its possible causes and solutions.
Although, the current state of AI and cloud is far from acing the prescriptive and cognitive intelligence but the path to achieve the same is clearer than before.
Why Intelligent Cloud is Edging over Traditional Methods
Now that you know what the trends say and what the future holds. It is also important to comprehend how this technological change will impact the users (in this case companies) and why should they ditch the existing infrastructural models and make the cloud switch.
Let’s factor in all the deciding parameters step by step to evaluate the merits and demerits of the cloud with on-premise ML model.
1. Hardware Requirements
Building your own deep learning environment from scratch is a tedious process. The general-purpose computing resources are not sufficient to process the complex and high volume of ML data. Thus, instead of CPU, you need a high-end Graphics Processing Unit (GPU) to meet the computing requirements of the ML algorithm. It multiplies the processing time by 2-3x, in comparison to CPU.
On the other hand, Cloud ML environment doesn’t demand much effort. With their expertise in ML, machine learning-powered cloud solution providers offer a pre-configured cloud environment including GPUs, FPGAs for specialized hardware acceleration, high-memory instances and more. With all the hardware taken care of, businesses need not worry about any additional setup or configuration.
In addition to trimming the configuration efforts, it also cuts down the cost of the whole hardware setup to a bare minimum. Plus, as the resources are shared between many users on the cloud, the users only have to pay for the chunk of resources they use. Thus, cloud setup is also light on the pocket in contrast to the on-premise setup.
2. Machine Learning Skills
To make use of on-premise ML setup, one needs an in-house team of proficient developers who can handle heavy data volume and code advanced ML training programs.
Python, R, Lisp, Prolog and Java are just some of the programming languages that every ML developer must be skilled at, in order to build advanced ML models.
However, easier said than done, finding developers with such a niche skillset is hard. There is a large dearth of resources in the tech industry as AI has gained popularity only in the last few years.
Whereas, while using an ML-powered cloud solution, you do not require high development expertise. Service providers offer ready-made intelligence in the applications and workflows. AI services are seamlessly integrated into the hosted applications to impart intelligence. The most common use cases of the intelligence one can experience while using ML-powered cloud solutions are in the form of personalised recommendations, added security, image and video search, and more.
All the development is automatically handled by the cloud ML providers. Moreover, providers like Microsoft Azure also provide an intuitive interface with drag and drop options to make the solution easier to use and understand for the businesses.
3. Wide Dataset
Data is the staple of Machine Learning. It is the base to all the ML predictions, thus businesses need to be extra cautious towards the quality of the data they supply to the ML systems. In the case of on-premise systems, the data collected is restricted to the users of the on-premise system i.e., employees of the concerned organisation.
Also, before using the data for training, a significant amount of time is invested in gathering, cleaning and categorizing the data. And the time and effort in this process are multiplied as the data size expands.
However, in the case of cloud systems, the intelligence scope is wide. Cloud providers like Microsoft, Google and Amazon collect a broad range of raw and processed data from scores of sources every day. Due to which, they are equipped with a larger dataset to supply into their ML systems that result in high precision of imparted intelligence.
Furthermore, cloud systems are adept at handling massive volumes of data. So whether your data size shrinks or expands, cloud ML systems can handle both scenarios with equal ease without hampering the intelligence or efficiency of the process.
Intelligent cloud conclusion
When two pioneering technologies – cloud & ML – join hands, the result has to be path-breaking. Both of these technologies have disrupted the technological landscape in unprecedented ways, and thus one cannot neglect the immense influence they will have on the next generation of technological innovation.
Having said that, it is also important to understand that traditional or on-premise systems will not become obsolete. Organisations that collect a hefty amount of data, sensitive user data or data that demands dedicated and complex machine learning algorithms will continue to use the on-premise infrastructure. However, this privilege will only be restricted to big enterprises that can afford to invest in the on-premise setups.
While the others (an increasing number) with a restricted scope of budget and resources will continue to rely on machine learning-powered cloud solutions to exploit the capabilities of ML.