Cloud computing has proved to be a boon during the COVID19 pandemic, which rattled global businesses. It helped organizations keep the functioning of global supply chains intact and effectively manage the remote workforce during the pandemic.
Organizations can expect a bigger role from cloud computing in the days to come, increasing scalability, business continuity, and cost-efficiency.
But, there is a thing to note. Just implementing a cloud solution will not work to help you see an improved business performance. Instead, it would help if you implemented it in a way that matches your business goal in the long term.
During the pandemic, technology needs for businesses became more complex as companies became decentralized.
Cloud Computing Trends to Shape Businesses
Let us explore what cloud computing trends are in the pipeline for the days to come:
Global Public Cloud Infrastructure Market to Go Up to USD120 Bn
Forrester Research says that the global public cloud infrastructure market will reach USD 120 billion in 2021. The growth emanates from the cloud taking center stage during the recovery from the pandemic.
Given the prevailing trends, the aggressiveness of businesses moving to the cloud will increase further in the days to come. With the increasing adoption of cloud solutions, cloud solutions providers will see an increase in their revenues.
Indications of global cloud spending to increase more in 2021, research firms make ambitious projections. For example, Garter forecasts that end-users global public cloud spending will grow 18 percent to USD 304.9 billion in 2021, up from USD 257.5 billion in 2020.
The pandemic has accentuated the inherent value proposition of cloud solutions. Due to the on-demand usability, scalable cloud models help businesses achieve cost efficiency and business continuity. And the advantages prompt businesses to accelerate their digital business transformation plans.
You can expect cloud adoption among businesses to grow more than ever in the coming days.
The software as a service (SaaS) segment will remain a large segment for end-user cloud IT spending. According to Garter, the SaaS segment is likely to grow to around USD 55.5 billion by 2021.
Garner further said that the application infrastructure services market would be driven by the need to access high-performing and scalable infrastructure through modern and cloud-native applications to manage remote workforces.
As the cloud facilitates much remote work, businesses will continue to migrate workloads and use more application infrastructure services resources to leverage the maximum financial advantage.
Due to the increased usage, cloud system infrastructure services (IaaS) spending will likely reach USD 65.3 billion in 2021.
Reshuffling Of The Big Three Cloud Providers
The stiff competition will make a reshuffle among the existing top three cloud service providers. China's Alibaba Cloud will likely displace Google Cloud to take the third spot for revenue in the global public cloud infrastructure market. However, Amazon Web Services and Microsoft will continue to remain as the first and second.
According to the calculations, Alibaba's cloud computing revenue grew 59 percent year-over-year to USD 2.19 billion for the quarter that ended 30 Sept 2021. The increased adoption of digitalization across industries and businesses in China drove the revenue from all types of industries.
On the other hand, Google Cloud's revenue increased to USD 3.44 billion from USD 2.38 billion in the corresponding quarter last year. The revenue sales from Google Cloud Platform, Google Workspace productivity tools, and other enterprise cloud services.
Google Cloud has already established itself as an enterprise-friendly cloud due to its efforts to improve ERP workloads, analytics, and account management.
Edge Assumes New Cloud Status
Edge is now the new cloud. Forrester predicts the emergence of new business models to facilitate the deployment of edge. Moreover, competition among cloud platforms and Artificial Intelligence(AI) deployment has facilitated the expansion of edge use cases.
Forrester further says that large vendors are increasing the usage of the edge with cloud-like solutions. In addition, content delivery networks and data center vendors are also providing edge computing services.
Given the growing popularity, buyers are expected to orient their cloud strategies towards the edge to leverage the innovative features and stay more connected.
The indications also reveal that the public cloud will not dominate, but it will play a moderate role. It is because they are based on massive data centers and tight architecture control. They are the opposite of what firms need to serve customers locally.
Notably, the centralized cloud is not gaining any popularity. Instead, the new real-time IT applications are the developments in serverless computing models and distributed service layers around the cloud.
What precisely businesses are looking at is, bridging the gap between the centralized cloud and end-users through the network edge. It helps provide low-latency application and content performance for all users, regardless of the work location.
Actuating a distributed edge strategy within a broader cloud computing framework will continue in the days to come.
If AI projects fail, it will cost businesses substantially. Therefore, companies need a perfect AI engineering strategy to ensure their AI projects do not fail.
Most businesses will fail to move AI projects from proofs of concept and prototypes to full-scale production without an AI engineering strategy.
At times, factors such as maintainability, scalability, and data governance restrict the success of AI projects. But, businesses can overcome these by implementing a strong AI engineering strategy that will improve their performance, scalability, interpretability, and reliability of AI models. As such, AI engineering can deliver good returns from AI investments.
AI is a part of the mainstream DevOps process in AI engineering rather than isolated projects.
Technically speaking, three fundamental pillars: DataOps, ModelOps, and DevOps support AI engineering.
DevOps deals primarily with high-speed code changes. But, it needs improvement to handle the dynamic changes in the code of AI projects.
Businesses have to apply DevOps principles across the data pipeline for DataOps and the machine learning model pipeline for MLOps to reap the benefits of AI engineering.
By now, you seem to know the latest cloud computing trends. We did our best, to sum up, the trends in the best possible manner.