Digital economy, no technology can support the independent development of the technology, which is why the iteration has 64 years of AI, 17 years of evolution edge computing, cloud computing Transmutation 14 years of cutting-edge technology today was ushered in full the main reason for the outbreak.
AI, edge computing, cloud computing relationship between the three
From a technical point of view, in the course of collision with the iteration, AI, edge computing, cloud computing three complementary only presents a picture of the current interconnection of all things, all things associated with wisdom , and the relationship between them can be simply a metaphor description:
If the cloud computing brains compared to computer intelligence system, the edge computing system is equivalent to the eyes, ears and hands and feet, AI is the system "smart" core analyzer. That is in the cloud era, cloud computing equivalent co-ordinator, edge computing is an important driver of one by one break, AI + edge computing cloud computing collaboration mechanisms to maximize efficiency.
Among them, as a use of the Internet anytime, anywhere, to a wide range of network access, resource sharing, and have unique advantages of technology and rapid scalability metrics, etc. , the cloud appeared to subvert the traditional technical architecture of IT systems. At the same time, it encompasses SaaS, Paas, laaS three services model that allows the development of many applications there is no need worry about the headaches of hardware, software and other issues, and enables companies and engineers to direct actions to resolve in the cloud. It Is, therefore, between the recent years, more and more companies choose the "cloud."
However, although cloud computing to accelerate application deployment and enhanced business agility and IT systems, but the era of things, the outbreak of massive data, if the data is uploaded cloud intelligent processing, will no doubt bring a lot of network bandwidth challenge, while areas such as intelligent autopilot in certain scenarios, intelligent medical equipment, and some time to return the data to the cloud, but need to be identified and analyzed and processed in real time immediately, which is applied to the edge of computing technology.
Is calculated edge refers to the network side edge or close to the source data, the integration of the network, computing, storage, and application platforms distributed processing capabilities, intelligent services nearest , in practical applications, it can be filtered and the sensor side of the data analysis send only the relevant data to the cloud, thereby realizing a data processing operation of the focus topical, immediate and short cycle. Based on this, may be understood as an extension of the edge calculated cloud, it is possible to further sink the cloud computing capacity to the edge.
In the data exchange complex, the simple point of view, the presence of AI is enabling a variety of platforms to build algorithms and models, and analyze what data needs to be uploaded to the cloud, what data needs to be independent and to quickly make a decision, so based on machine learning, signal processing and optimization and other means, the data collected by the device for training and learning, which allows the sensor system in constant iteration has the ability to adjust its internal algorithm according to the latest data, and expand its intelligence.
Under the interconnection of all things, AI how to fully penetrate?
Today, AI has become an inevitable trend of social development, and comprehensive AI want incoming intelligent terminal equipment, not only need the help of cloud computing, but also need the support edge computing, but at the same time bring good technology, we will also find application challenges come, in the moment, "how AI applications more efficiently at the edge of the calculation, and bring high-speed response capability" is quite concerned about the industry and the need to solve the problem.
As we all know, for the industry, developers and engineers, the Internet of things + AI era, want to use the application and development of sensor data, it requires machine learning, signal processing, data processing and optimization, and embedded engineering expertise related . And these technologies have the talent, whether it is for large companies or small companies, are hard to find and expensive.
So, if there are low-cost, high-efficiency quick solutions to help the industry better start from the computing side edge AI? Based on this, at the end of last year, artificial intelligence company Qeexo odd hand released a fully automated one-button platform --Qeexo AutoML, to fill the vacancies in the market for fully automated machine learning platform for embedded edge devices, allowing users to quickly on the edge Construction of the device using sensor data on machine learning solutions.
On Qeexo AutoML platform, the data can be pre-processing, feature extraction, selection model, optimize hyper-parameters, repeat the process a lot of work to verify the results, as well as the deployment model and other traditional machine learning processes required are automated, so to solve machine learning engineers scarcity problem will undoubtedly have a positive impact. In under a wave of AI developers how to quickly use Qeexo AutoML platform, to better break through challenging AI computing at the edge of practice ?
Here, CSDN invited to the Qeexo China area market leader Zhai Wei , with the industry more wearable areas, IoT sector and industrial applications engineers and senior management personnel who were on the platform automatically Qeexo AutoML developed for embedded targets practice and application of machine learning models platform lightweight, high-speed and high-performance.
Based on this, the engineers only need to enter a label raw sensor data, processing it into a platform will be suitable for machine learning data, and automatic feature extraction, ultra-parameter optimization, and implementation of machine learning algorithms to build the model, and then target embedded MCU to create a machine learning library. This model was created with streamlined code, processing speed, low power consumption advantages.
Qeexo China area market leader Zhai Wei
Zhai Wei, the company responsible for Qeexo machine learning products market in China to promote strategic planning and in. He graduated from Tsinghua University in postgraduate degree in Chinese Academy of Sciences. 20 years of technical, marketing and sales experience in China IC industry, has held senior management InvenSense China and the United States Cypress area, embedded processors and sensors for applications in the mobile phone products, home appliances, IoT and industry have in-depth It is covered. The company is currently assisting efforts to promote "universal AI in edge applications" mission.
Through this course, you can Get to the following knowledge:
1. Qeexo AutoML platform by complex and labor-intensive task automation, use sensor data to quickly create a machine learning algorithm.
2. This tool embedded data needed to create an AI algorithm science, machine learning, signal processing and optimization, embedded engineering expertise.
3. Qeexo AutoML platform using sensor data, automatic feature extraction, model creation and optimization, and machine learning to create a library for embedded MCU platform.
4. plant predictive maintenance, wearable, IoT, aged care, wisdom, family, animal tracking, and sensor data using all IoT applications to apply this tool.
AI era, why not welcome an efficient tool edge and on!
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