上海桑拿

"Big data" is a data set with a particularly large volume and data categories, and such data sets cannot be captured, managed and processed by traditional database tools. "Big data" first refers to data volumes? Large refers to a large data set, usually in 10TB? However, in practical application, many enterprise users put multiple data sets together, which has formed PB-level data volume; Secondly, it refers to the large variety of data, which comes from a variety of data sources, and the types and formats of data are increasingly rich. It has broken through the previously defined structured data category, including semi-structured and unstructured data. Secondly, the speed of data processing is fast, and the real-time processing of data can be achieved even when the amount of data is huge. The last feature is the high authenticity of data. With the interest of new data sources such as social data, enterprise content, transaction and application data, the limitations of traditional data sources have been broken, and enterprises increasingly need effective information power to ensure their authenticity and security.

Data collection: ETL tools are responsible for extracting data from distributed and heterogeneous data sources, such as relational data and flat data files, into the temporary middle layer, cleaning, converting and integrating them, and finally loading them into data warehouses or data marts, which become the basis of online analysis and data mining.

Access to data: relational database, NOSQL, SQL, etc.

Infrastructure: Cloud storage, distributed file storage, etc.

Data processing: NLP (NaturalLanguageProcessing) is a subject that studies the language problems of human-computer interaction. The key to natural language processing is to make computers "understand" natural language, so natural language processing is also called NLU (NaturalLanguage Understanding), also known as Computational Linguistics. On the one hand, it is a branch of language information processing; on the other hand, it is one of the core topics of artificial intelligence.

Statistics: hypothesis test, significance test, variance analysis, correlation analysis, t-test, variance analysis, chi-square analysis, partial correlation analysis, distance analysis, regression analysis, simple regression analysis, multiple regression analysis, stepwise regression, regression prediction and residual analysis, ridge regression, logistic regression analysis, curve estimation, factor analysis, cluster analysis, principal component analysis, factor analysis, fast clustering method and clustering method

Data mining: Classification, Estimation, Prediction, affinity grouping or association rules, Clustering, Description and Visualization, complex data type mining (Text, Web, graphics, video, audio, etc.)

Prediction: prediction model, machine learning, modeling and simulation.

Results: Cloud computing, tag cloud, diagram, etc.

To understand the concept of big data, we should first start with "big", which refers to the data scale. Big data generally refers to the amount of data above 10TB(1TB=1024GB). Big data is different from massive data in the past, and its basic characteristics can be summarized by four V’s (Vol-ume, Variety, Value and Veloc-ity), namely, large volume, diversity, low value density and high speed.

First, the data volume is huge. From TB level to PB level.

Secondly, there are many types of data, such as weblogs, videos, pictures, geographical location information, and so on.

Third, the value density is low. Take video as an example. During continuous monitoring, the data that may be useful is only one or two seconds.

Fourthly, the processing speed is fast. 1 second law. This last point is also fundamentally different from the traditional data mining technology. Internet of Things, cloud computing, mobile Internet, Internet of Vehicles, mobile phones, tablets, PCs, and various sensors all over the globe are all data sources or ways of carrying them.

For the whole artificial intelligence industry, there is a great demand for AI technology in the fields including driving, security, finance, industry, medical care, education, etc. The rapid development of AI technology based on machine learning depends on the richness of the underlying big data, and a powerful model needs a data set with a large number of samples as its foundation. The quality and diversity of data will have a significant impact on the success or failure of algorithm models. The delivery of high-precision AI data not only helps the AI industry to land in scenes, but also brings a better user experience.

At the data level, with the development of AI technology, the data scale is constantly improving. According to IDC’s calculation, the global data scale will reach 163ZB； in 2025; At the same time, the AI data service industry has entered the stage of deep customization, and the service of data customization is carried out according to different scenarios and requirements, and the AI data requirements also transition from general simple scenarios to personalized scenarios.

In order to solve the practical problem of AI industrialization, cloud measurement data summed up many experiences and solutions, and used them in practice to help the development of the whole artificial intelligence scene application. Through its own technology, it has overcome the difficulties, designed scientific and standardized data processing processes from task creation to final acceptance, and flexibly met the diverse and high-precision data needs of customers. It has successively launched products and services such as "data scene laboratory", "AI data set management system" and "cloud measurement data annotation platform", providing high-quality, scene-based and large-scale processing of perceived data for many AI-related enterprises such as intelligent driving, smart city, smart home, smart finance and new retail.

Of course, it is not easy to keep the leading position of technology and industry in the tide of artificial intelligence. From the perspective of attack and exploration, it is not difficult to see that the reason why cloud measurement data can become an industry leader is not only due to the toughness of technology and product strength, but also the homeopathic development of service model and service concept, thus continuously injecting new vitality into the artificial intelligence industry and providing new kinetic energy for development.

First of all, data came into the market when the industry was on the rise, and the cloud measurement data with the first-Mover advantage was not satisfied with the dividends at that time, but constantly increased the technical input and improved the production efficiency by improving the technical level. Give full play to the power of "underlying technology+service capability" and provide end-to-end training data service solutions in autonomous driving, smart home, smart city and smart finance and other industries.

At the same time, cloud measurement data keeps forward-looking forecast on the development trends of hot industries and technologies, and prepares relevant tool chains and data service capabilities in advance to ensure adequate preparation to meet new AI data requirements. In the current AI data industry chain, there is a keen discovery of cloud measurement data, and there is still a lack of a systematic data solution for AI engineering. However, this systematic data solution for AI engineering is needed by many industries. In this context, the cloud measurement data industry launched a new generation of data solutions for AI engineering, which was undoubtedly a timely rain for many industry customers and solved their actual needs.

For this reason, cloud measurement data has launched a new generation of data solution for AI engineering. Through the mature data management and labeling platform, this solution can complete system integration with enterprises, support enterprise-defined pre-labeling, algorithm interface, personnel management, project management system and secure delivery of software and hardware support. Under the labeling environment that ensures data privacy and security, it highly supports the efficient circulation of data required by enterprises, continuously performs data processing tasks, and improves the large-scale production efficiency.

For example, in the field of automatic driving, it can realize Data cleaning and labeling in the data closed loop of DataOps (that is, the combination of data and Operations) of automobile enterprises, and improve the circulation efficiency by 2 times compared with the original process; In the aspect of retail goods inspection, through the cloud measurement data labeling platform, the container inspection data continues to flow back, and visual review and modification are carried out based on the pre-labeling results of the algorithm, which improves the efficiency by 3 times compared with manual labeling.

"Walk alone fast, go far". In the era of industrial intelligence, we can’t just rely on one enterprise to fight alone. The double value of industry and society will produce compound interest effect. Cloud measurement data also knows this well. It is also actively promoting the standardization of artificial intelligence data industry, and has participated in the compilation and release of "Requirements and Methods for Marking Point Cloud Data of Intelligent Networked Car Lidar" and "Requirements and Methods for Marking Image of Intelligent Networked Car Scene Data", contributing experience and wisdom to industrial intelligence, and promoting the construction of standardization system in the vertical field of AI data service. In addition, it also participated in the first series of standards of "Model/MLOps Capability Maturity Model", which filled the gap of the development and management standards of machine learning projects at home and abroad.

Summary:

As the vanguard of artificial intelligence data services, cloud data is actively promoting the accelerated development of AI training data services, contributing experience and wisdom to industrial intelligence, thus becoming a new paradigm of industry development. I believe that next, cloud measurement data will continue to improve. While continuously enriching its own service capacity building and deep cultivation technology, it will maximize the value of training data and deliver more excellent data support for artificial intelligence scenes.

Michael Joseph Jackson can absolutely be called a symbol of a era in the history of music. His music and dance have affected generations.

Perhaps he is too talented, its life has spent the dispute, among which the skin color and appearance are the largest. Even after many years of death, some people still assume that if he does not "bleach" skin and cosmetic, will it be?

Michael Jackson was killed in the King of Samway, Klin, 1992.

Below, you will look at a group of photos, look at his life, in each period. It is worth noting that the last photo is calculated by artificial intelligence, if he does not have a skin, it looks at 50.

In 1965, Jackson Jackson was on the stage for the first time and started the legendary life.

In 1978, Michael and met the music producer Quincy Jones, two people became a boring friend.

This is 1980, and he created a photo of the famous music "treasurer".

In 1983, Michael performs "space step" on the stage, shocked the world.

In 1988, Michael and Wang Hao Dianna met, creating MTV "Moonwalker", becoming the best-selling video belt in history.

In 1991, Jackson debuted in Africa, being crowned as "Sani King". At this time his skin has become white, and it has become a turning point for a lifetime.

In 1996, he divorced with a daughter of the Cat.

In 2005, after a year of forensics, "Love Book Case" finally reported that 14 accused of him were not guilty.

In 2009, 50-year-old Michael died in the United States.

If he doesn’t have a cylinder, what will he look like when he is 50 years old? Here’s this photo is artificially intelligent to give an answer.

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