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Amazon’s operating revenue reached US $18.44 billion in Q1 2022, a year-on-year increase of 37% From Amazon financial report

The following is the Amazon’s operating revenue reached US $18.44 billion in Q1 2022, a year-on-year increase of 37% From Amazon financial report recommended by recordtrend.com. And this article belongs to the classification: cloud computing, Enterprise financial report.

Amazon, the originator of e-commerce and the retail giant, should add a label. According to the latest financial report of Q1 2022, cloud computing business has become the main growth and profit source of Amazon: the operating revenue in the first quarter increased by 37% year-on-year to US $18.44 billion; Operating profit increased by 57% year-on-year to USD 6.52 billion; The profit margin was 35.3%, higher than 29.8% in the previous quarter and 30.8% in the same period last year.

You know, Amazon is a behemoth in the global cloud computing market, with a public cloud market share of 38.9%, exceeding the sum of the last three. The large volume still has a high growth rate, which makes the expression “growth myth” even appear in the market.

△ Gartner 2020-2021 IAAs public cloud market share data

From another perspective, Amazon cloud technology in Gartner ® Magic Quadrant released ™ The for cloud AI developer services report also ranks in the leader quadrant.

As for what lies behind the “growth myth”, innovation must be mentioned.

Especially in recent years, cloud computing, as an infrastructure that provides AI with large computing power and big data storage and transmission capacity, has also begun to be deeply integrated with AI.

Out of the scope of cloud computing, Amazon cloud technology has also been rated as the first in innovation in all domestic AI development platform application markets.

△ frost Sullivan & China AI development platform market report in 2021 by toubao Research Institute

This aspect of Amazon cloud technology has always been easy to be ignored in China, only because cloud computing does not directly contact with consumers, but provides support in all aspects of daily life.

This change in financial data will serve as an eye-catching sign and will become the starting point for the market to reverse its understanding of Amazon.

For a long time, Amazon cloud technology has been promoting the implementation of new technologies in China, helping Chinese enterprises go to sea and localize foreign enterprises, and promoting the digital and intelligent transformation of traditional industries, which will also be seen by more people.

New technology landing

Smart cars are certainly one of the hottest industries to talk about landing at present, but it is difficult to land only in the aspect of automatic driving.

According to the research of RAND Corporation in the United States, the automatic driving algorithm needs at least 17.7 billion kilometers of driving data to improve the algorithm in order to reach the level of human drivers.

Not to mention the immensity of reaching the human level, according to the most recognized SAE automatic driving classification standard, it also takes 20million kilometers of road test mileage to reach L3 level.

The data scale generated by the road test of tens of millions of kilometers should reach EB level, that is, the common TB is multiplied by 1024 and then multiplied by 1024.

What’s more annoying is that the data formats required by different links are not unified. For example, s3/nfs format is required for data import, HDFS format is required for data preprocessing, NFS format is required for AI training, and simulation and model verification are also required

△ from the white paper on data intensive supercomputing Technology issued by the high performance computing Professional Committee of the Chinese Computer Society

Many start-ups in the smart car industry have limited human and material resources. If they want to build their own it systems, they can basically do nothing else. On the one hand, the scale of their own construction is difficult to keep up with the rapid development of business, on the other hand, operation and maintenance involve a lot of energy.

In order to focus on core technology research and development, migration to third-party cloud computing services has become a natural choice.

For example, the L4 self driving star head company, wenyuanzhixing, was established in 2017 and began to deploy data processing and machine learning platforms on the Amazon cloud technology cloud in early 2019.

One of the biggest gains of this move is to greatly shorten the training time of the autopilot model. According to Huoda, director of the data team of wenyuanzhixing, it takes about 1-2 weeks for the industry to complete a training model, while it only takes 12 hours on the Amazon cloud technology platform.

In addition, the total cost of ownership (TCO) of the system has been saved by 1/3, the operation and maintenance efficiency has been improved by 50%, and the overall security and reliability of the system have been guaranteed.

With such advantages, Wenyuan Zhixing launched its first shot at the commercialization of autonomous driving in November 2019, and launched the country’s first fully open robotaxi operation service in Guangzhou.

The domestic autonomous driving companies that also use Amazon cloud technology machine learning platform include momenta, Zhijia technology, etc., and Aurora, Mobileye, Tucson future, etc. are also used globally.

Providing each customer with cloud computing resources alone is not the whole consideration of Amazon cloud technology.

After accumulating a lot of industry experience, Amazon cloud technology launched two targeted new services in December of the 21st: Amazon IOT fleetwise, which solves data collection problems, and Amazon for automotive, an industry solution.

Amazon sagemaker canvas codeless machine learning platform is also launched for groups with zero machine learning experience.

Amazon sagemaker canvas visualizes many steps of the machine learning model as an interactive UI, enabling business, human resources, finance and other departments to quickly generate the machine learning prediction model and solve problems in their work without writing a line of code.

For example, BMW, a traditional car company, has promoted ai/ml technology to more than 600 applications in the group’s actual business processes through Amazon sagemaker canvas codeless machine learning platform, covering multiple scenarios from the production line to the sales end.

15million BMW cars are connected to this platform and millions of kilometers of data are generated in a day, which are analyzed and predicted by Amazon sagemaker canvas.

Chinese enterprises going to sea and localization of foreign enterprises

AI and data analysis are not only patents for cutting-edge industries, but also useful in the broader consumer and Internet industries.

Among them, both intelligent manufacturing and digital economy have broad prospects under the double dividend of capital and policy.

Compared with the autonomous driving industry, overseas enterprises face more troubles. They need a unified global infrastructure. Facing the risks brought by cross-border payment, they also need to meet the increasingly stringent data security compliance requirements of various countries.

As a result, because of its own positioning, Amazon cloud technology is especially favored by Chinese enterprises that want to go abroad for development and foreign enterprises that want to land in China.

Amazon cloud technology ranks second in the statistics of China’s public cloud market share of iResearch consulting, which includes sea going business.

Examples of Chinese enterprises going to sea include oppo, whose overseas shipments of smart phones have accounted for more than half, and the wearable smart device market is also under development.

Oppo’s AI assistant has a monthly life of more than 100 million. How to reduce AI reasoning cost and improve AI reasoning efficiency is the key.

In addition to finding ways to optimize the algorithm, we can also ask for a foreign aid, which is a special reasoning chip.

Oppo finally chose to deploy the small cloth assistant on the Amazon EC2 INF1 instance, using the inferentia reasoning chip developed by Amazon cloud technology, which can reduce the single reasoning cost by up to 70% compared with the previous generation of GPU based instances.

Under the two scenarios of q&a and chat, Xiaobu assistant can save about 35% of reasoning, and reduce the end-to-end delay by as much as 25%.

The workload of migrating to a new chip is also small. With the Amazon neuron development kit, only minimal code changes are required.

The representative of localization of foreign enterprises is Daniel Wellington, a watch and jewelry manufacturer from Europe.

After entering the global market, they found a common problem, that is, there is a time difference between the headquarters and global consumers.

For example, the person in charge of reviewing the customer’s repair request or return application is still late at night, so we have to wait until the next day. The consumption experience is very poor.

Later, they created an automated process based on the Amazon rekognition image recognition API. The return speed based on image recognition was 15 times faster than before.

From this small case, we can also see that there is more room for the development of cloud computing.

Compared with a few enterprises that have AI technology and need computing resources, more enterprises need to customize their own business processes based on their existing AI capabilities.

In this regard, in addition to Amazon rekognition mentioned above, Amazon cloud technology also provides a series of related products.

Amazon personalize presets the necessary infrastructure and algorithms of the recommendation system, provides API interfaces, and can quickly build personalized recommendation applications. Lotte Mart supermarket uses it to increase the number of products that customers have never purchased by 40%.

Amazon connect brings AI agent scheduling, risk fraud detection, emotion analysis and other capabilities to the contact center, which can save up to 80% of the cost compared with the traditional contact center solution.

Amazon lex, which opens the same technology as Alexa voice assistant, can build, deploy and manage customized voice chat robots, and can also natively integrate the contact center built by Amazon connect.

Using these products requires no professional AI knowledge, and traditional software developers can quickly build AI applications.

But what about those enterprises that lack the traditional IT development capability?

Intelligent transformation of traditional industries

Digital transformation and intelligent upgrading are hot words in recent years.

According to IDC, an authoritative market research institution, AI will become an indispensable part of all enterprises by 2024.

As more and more traditional industries, such as industrial manufacturing, logistics, energy, transportation and agriculture, demand for intelligent transformation bursts out, then 25% of AI investment will promote innovation in the form of outcomes-as-a-service.

For the manufacturing industry, an important application of AI is demand forecasting.

In particular, the capricious epidemic has caused unprecedented fluctuations in customer demand and upstream and downstream supply chains in the manufacturing industry.

Foxconn cooperated with Amazon machine learning solutions lab to develop an end-to-end demand forecasting model for one of its factories with Amazon forecast time series forecasting service.

This solution improves the forecast accuracy by 8% and will save the plant $553000 per year.

Amazon monitron can also provide end-to-end AI capabilities. Its basic usage is to monitor the abnormalities of industrial equipment. More advanced is to discover the real problems of equipment through machine learning before they occur.

The professional term is “predictive maintenance”. On the one hand, it can prevent the unexpected shutdown of a certain equipment from affecting the operation of the whole production line and possible safety problems. On the other hand, timely maintenance before failure can also increase the service life of a single equipment.

This scheme has been used by musical instrument manufacturer fender, Ge gas power and other industry customers.

The integration of cloud, data and intelligence is the answer of this era

Having seen so many cases from all walks of life, it is not difficult to summarize two laws.

First, from high-tech to the Internet, consumption to more traditional industries, they are inseparable from the close combination of cloud computing, AI and data analysis.

Data is often compared to “digital oil”, which is the most important factor of production in this era. AI algorithms need to mine more value from massive data, and AI algorithms need cloud computing to provide a lot of computing power.

This is the main logic for the global cloud computing market to grow from 0 in 15 years to 705billion dollars in 2021.

Second, industries that are farther away from technology have greater demands in intelligent upgrading, and they also need complete, end-to-end solutions.

In this way, the integrated development of big data, artificial intelligence and cloud computing is the answer to this era.

So during this period of time, we have seen that the original big data centers in various places have been upgraded into intelligent computing centers. Companies starting with AI algorithms, such as Shangtang, have begun to build their own computing power supply system, while cloud computing companies are developing their own AI chips and AI technologies.

Among these players, the use of comprehensive solutions, comprehensive ai/ml tools, mlops methodology and services to lower the threshold for AI use is the advantage of Amazon cloud technology and the driving force behind the “growth myth”.

In terms of the performance of Amazon cloud technology in the domestic market, its technical system has not been acclimatized. Instead, it has become a connecting point for Chinese enterprises to go to sea and foreign enterprises to take root.

So many cloud services mentioned above are actually just the tip of the iceberg in this technology system.

Every year, Amazon cloud technology will launch thousands of new services, including 250+ AI related services.

This number makes relevant practitioners feel like “you slow down, I can’t learn”.

Fortunately, Amazon cloud technology will sort out and present recent trends through the innovate conference every year.

It is reported that this year’s innovate conference is based on the theme of “new engine of artificial intelligence”, free registration and online participation.

If you want to know about the AI technology side of Amazon cloud technology, you might as well visit it next week.

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