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AI the necessary ability of artificial intelligence engineers
AI the necessary ability of artificial intelligence engineers
In recent years, the application of artificial intelligence technology in all walks of life is becoming more and more popular, and the related professional and technical personnel are also in short supply. The major companies or the enterprises are recruiting AI talents with great money.
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According to a recent statistics, the average annual salary of AI related positions is 300 thousand yuan -60 yuan, and even a long time can reach an annual salary of millions. The following is the data statistics from some recruitment websites, 56 positions (60-100, 1 million + two) of the highest salary, and 30 of the master's degree and 53%. The average ratio of the master degree of AI engineers is 28.6%, which is twice as high as that of the master.
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Now it can be said that machine learning algorithm engineer is the best era, and the demand for all kinds of talents is very strong in all walks of life. Typical segments include the following segments:
Recommendation system: to solve the problem of efficient matching and distribution of information in massive data scenes, the machine learning plays an important role in the recall of candidate sets, the sorting of results, and user portrait.
Advertising system: there are many similar places to the recommendation system, but there are also significant differences. It is necessary to consider the interests of the advertisers at the same time in addition to the platform and the users. The two party has become three parties, making some problems complicated.
Search system: many of the infrastructure and top ranking of the search system use machine learning technology, and in many websites and App, search is a very important traffic entrance, the optimization of machine learning to search system will directly affect the efficiency of the whole website.
Wind control system, especially Internet financial risk control, is another important battlefield of machine learning in recent years. It is not an exaggeration to say that the ability to use machine learning can largely determine the wind control ability of an internet financial enterprise, and the ability of wind control itself is the core competitiveness of the business security of these enterprises.
The so-called "higher wages, greater responsibilities", the requirements for Algorithm Engineers in enterprises are also gradually improving. Therefore, this paper talks about the learning and growth path of machine learning algorithm engineer, and gives some suggestions and information about learning.
A necessary ability item for Machine Learning Algorithm Engineers
It is not a simple thing to be a qualified development engineer. It needs to master a series of abilities from development to debugging to optimization. Each of these abilities needs enough effort and experience. To be a qualified Machine Learning Algorithm Engineer (hereinafter referred to as the algorithm engineer) is more difficult, because in mastering the general skills of the engineer, it is necessary to master a small knowledge network of machine learning algorithms.
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Below we will be a qualified Algorithmic Engineer to take apart the skills needed to look at what skills you need to master to be a qualified Algorithmic Engineer.
01: basic development ability
The algorithm engineer, first of all, needs to be an engineer, so we have to master all the capabilities that all development engineers need to master. In most jobs of most enterprises, Algorithm Engineers need to be responsible for the whole process from algorithm design to algorithm implementation and algorithm on-line.
02: the basis of probability and statistics
Probability and statistics are one of the cornerstones of the field of machine learning. From a certain point of view, machine learning can be viewed as a systematic thinking and cognitive approach to the uncertain world based on probability thinking. Learning to look at problems from a probabilistic perspective and using probabilistic language to describe problems is one of the most important foundations for in-depth understanding and skilled use of machine learning technology.
In terms of statistics, some commonly used parameter estimation methods also need to master, such as maximum likelihood estimation, maximum a posteriori estimation, EM algorithm and so on. These theories, like optimization theories, can be applied to all models. These distributions run through various models of machine learning, and exist in various data in the Internet and in the real world, understanding the distribution of the data, and knowing what to do with them.
03: developing language and development tools
In recent years, Python can be said to be the most popular language in the field of data science and algorithms. The main reason is that it has a low threshold, easy to hand, and a complete tool ecosystem, and the support of various platforms is also better. But in the field of model training, there are some more focused tools that give better training accuracy and performance, such as LibSVM, Liblinear, XGBoost, etc. For large data tools, the mainstream tools for off-line computing are still Hadoop and Spark, and Spark Streaming and Storm are also the most popular options for real-time computing.
04: machine learning theory (most important)
Although the Open Source Toolkit is more and more open now, it doesn't mean that algorithm engineers can ignore the learning and mastering of machine learning basic theory. There are two main meanings in doing this:
Only by grasping theories can we apply all kinds of tools and skills flexibly instead of copying them. Only on this basis can we really have the ability to build a set of machine learning system and optimize it continuously. Otherwise, it can only be regarded as a machine learning, brick worker, not qualified engineer. Problems will not be solved, let alone optimize the system.
The purpose of learning machine learning's basic theory is not only to learn how to build machine learning system, but what's more important is to learn how to build machine learning system.
.
According to a recent statistics, the average annual salary of AI related positions is 300 thousand yuan -60 yuan, and even a long time can reach an annual salary of millions. The following is the data statistics from some recruitment websites, 56 positions (60-100, 1 million + two) of the highest salary, and 30 of the master's degree and 53%. The average ratio of the master degree of AI engineers is 28.6%, which is twice as high as that of the master.
640? Wx_fmt=jpeg&wxfrom=5&wx_lazy=1
Now it can be said that machine learning algorithm engineer is the best era, and the demand for all kinds of talents is very strong in all walks of life. Typical segments include the following segments:
Recommendation system: to solve the problem of efficient matching and distribution of information in massive data scenes, the machine learning plays an important role in the recall of candidate sets, the sorting of results, and user portrait.
Advertising system: there are many similar places to the recommendation system, but there are also significant differences. It is necessary to consider the interests of the advertisers at the same time in addition to the platform and the users. The two party has become three parties, making some problems complicated.
Search system: many of the infrastructure and top ranking of the search system use machine learning technology, and in many websites and App, search is a very important traffic entrance, the optimization of machine learning to search system will directly affect the efficiency of the whole website.
Wind control system, especially Internet financial risk control, is another important battlefield of machine learning in recent years. It is not an exaggeration to say that the ability to use machine learning can largely determine the wind control ability of an internet financial enterprise, and the ability of wind control itself is the core competitiveness of the business security of these enterprises.
The so-called "higher wages, greater responsibilities", the requirements for Algorithm Engineers in enterprises are also gradually improving. Therefore, this paper talks about the learning and growth path of machine learning algorithm engineer, and gives some suggestions and information about learning.
A necessary ability item for Machine Learning Algorithm Engineers
It is not a simple thing to be a qualified development engineer. It needs to master a series of abilities from development to debugging to optimization. Each of these abilities needs enough effort and experience. To be a qualified Machine Learning Algorithm Engineer (hereinafter referred to as the algorithm engineer) is more difficult, because in mastering the general skills of the engineer, it is necessary to master a small knowledge network of machine learning algorithms.
640? Wx_fmt=png
Below we will be a qualified Algorithmic Engineer to take apart the skills needed to look at what skills you need to master to be a qualified Algorithmic Engineer.
01: basic development ability
The algorithm engineer, first of all, needs to be an engineer, so we have to master all the capabilities that all development engineers need to master. In most jobs of most enterprises, Algorithm Engineers need to be responsible for the whole process from algorithm design to algorithm implementation and algorithm on-line.
02: the basis of probability and statistics
Probability and statistics are one of the cornerstones of the field of machine learning. From a certain point of view, machine learning can be viewed as a systematic thinking and cognitive approach to the uncertain world based on probability thinking. Learning to look at problems from a probabilistic perspective and using probabilistic language to describe problems is one of the most important foundations for in-depth understanding and skilled use of machine learning technology.
In terms of statistics, some commonly used parameter estimation methods also need to master, such as maximum likelihood estimation, maximum a posteriori estimation, EM algorithm and so on. These theories, like optimization theories, can be applied to all models. These distributions run through various models of machine learning, and exist in various data in the Internet and in the real world, understanding the distribution of the data, and knowing what to do with them.
03: developing language and development tools
In recent years, Python can be said to be the most popular language in the field of data science and algorithms. The main reason is that it has a low threshold, easy to hand, and a complete tool ecosystem, and the support of various platforms is also better. But in the field of model training, there are some more focused tools that give better training accuracy and performance, such as LibSVM, Liblinear, XGBoost, etc. For large data tools, the mainstream tools for off-line computing are still Hadoop and Spark, and Spark Streaming and Storm are also the most popular options for real-time computing.
04: machine learning theory (most important)
Although the Open Source Toolkit is more and more open now, it doesn't mean that algorithm engineers can ignore the learning and mastering of machine learning basic theory. There are two main meanings in doing this:
Only by grasping theories can we apply all kinds of tools and skills flexibly instead of copying them. Only on this basis can we really have the ability to build a set of machine learning system and optimize it continuously. Otherwise, it can only be regarded as a machine learning, brick worker, not qualified engineer. Problems will not be solved, let alone optimize the system.
The purpose of learning machine learning's basic theory is not only to learn how to build machine learning system, but what's more important is to learn how to build machine learning system.