Machine Learning & Artificial Intelligence Developers Salary, Resume and Necessary Skills?
Here Mobilunity is going to explain some basic industry-related terms, discuss what industry adopted (or plan on adopting) the technology, and explore the software developer wage in such industries as machine learning and artificial intelligence.
Artificial Intelligence vs Machine Learning: Definition and Practical Application of Both
What Is AI?
Some people which studied literature are thinking about Talos, others think about Terminator, but let’s get more serious. The concept is not new, a theoretical base was developed in 30th years of the 20th century. The basic idea came from biology and math, but we will try to keep it simple. The biology part is the basic layout which consists of neuron and perceptron. Data is passed to perceptron and there translated to a number. The neuron has the formula which is applied to the number and the number is used to make a decision. For the math part, neuron is a function, which processes the part of the data and results are like rating and is passed through layers of neurons. This is a simplified explanation, but it’s not new technology, neural networks have been used in photo cameras for years.
What Is Machine Learning?
Machine learning is the way to reach artificial intelligence. The term artificial intelligence means that the machine can show intelligence, which means that it can learn. This learning process differs from humans’, but the basic idea is similar.
Machine learning is the way to teach the machine and gain experience which can be adopted later or be loaded to another machine. There are two phases of machine learning. First, is to write the algorithm itself and the second one is to teach it, (with a ‘teacher’ which will point the correct answer). Generally thousands, it takes thousands of runs after which the algorithm is starting to show at least 80% of accurate answers.
Machine learning is basically creating algorithms and software that is using experience. The experience or, as it is called, ‘data model’ can be saved to some data storage (sort of an advanced case of content-driven development). Based on this model, the same algorithm can be adopted to be used in different runtimes or even for different tasks.
Writing the algorithm is complex and requires a mathematical background since the all the incoming data (images, text data, sound) should be processed to numbers or like numbers structures called tensors and then passed to neural networks, where it can be processed. Also, the amount of data that should be used to get reliable results must be huge, in some cases, it can include petabytes of information.
What is the difference between machine learning and artificial intelligence?! Basically, these are almost the same, AI is more advanced usage of neural networks. Machine learning is a form of artificial intelligence that learns patterns in data and uses them to predict patterns in new data.
Why It Is so Popular Now?
Basically, the algorithm can learn, and the calculated numbers can be saved as experience. It means that once the algorithm has enough experience, it can be scaled for much bigger and complex tasks.
About ten years ago computers became able to handle big chunks of data and do complicated calculations, so neural networks can be used for more elaborate tasks. It’s used now for classification of data, e.g. computer vision. Apple Face Recognition, even the advanced cases of cameras that can find a person (or group of people) and even detect the identity (like popular social media that learns to find you in photos where you haven’t even been tagged), or count the people on the video stream ー all this presents infinite opportunities in the future.
Practical Application of AI and ML in Business
Machine learning is one form of technology that allows organizations to make better decisions without human intervention. When used correctly, machine learning can help businesses become more efficient. It allows businesses to analyze bigger and more complex data and deliver faster, more accurate results.
How Can a Business Entity Benefit from AI and ML?
AI and ML solutions are extremely popular now, and, frankly speaking, the buzz won’t go away for a long time.
Google Cloud reports that among CIOs of medium-to-big companies 21% already have pilot projects that involve AI and machine learning within their domain, 25% will adopt it on medium-to-long term projects, and 85% will have pilot or pet projects by 2020.
Among the ‘traditional’ applications (or at least those that already have proved its efficiency and have real-life cases) are:
- Cheaper analytics
Human resources can cost a fortune when it comes to data analysis. Huge amounts of information can take months for human beings to analyze, process and spot a trend (when its already gone). AI can do that much faster and spot tendencies and patterns where the human brain could not.
No, we are not offering Sofia the robot to conduct the interview. However, artificial intelligence can save time browsing through numerous CVs and applications, sorting candidates that better match to the set parameters.
- Technical customization
As much as we all love autofill option, AI can speed up interactions with websites figuring out your name/e-mail and improving the overall experience.
We are not talking about predictions about the future, but rather first-hand information about customer’s experience with the product, ways they feel about it or potential risk of client loss.
When it comes to security, there is no ‘too-much’. Apart from being able to learn what a customer wants, AI can also spot someone who just pretends to be one and use technology like generative adversarial networks react to attacks in real time (based on the analysis of situation).
Industries That Utilize AI and ML the Most
Industries of all types are using machine learning technology to operate more efficiently and stay ahead of their competition. Some examples of industries who have already unveiled the value of machine learning are financial services, health care, oil and gas, government, marketing and sales, and transportation. While machine learning has been around for a long time, today’s version is much different than the machine learning we knew of years ago. Machine learning in finance, healthcare, government, marketing will change the world.
According to the LinkedIn Emerging Jobs Report, AI skill has penetrated numerous industries and shows significant growth in demand over a period of 3 years.
- Computer Software
- Information Technology & Services
- Financial Services
- Higher Education
- Consumer Electronics
Among the use cases that are explored right now are computer vision, data classification, prediction, etc. AI is used for financial predictions and detecting risks since it’s based on big amounts of data (mostly transaction statistics). It’s used in healthcare to classify patients, to find co-dependency between physical parameters and illnesses, or to flag critical conditions before they happen. And let’s not forget about the military industry that uses AI to make weapon smarter and more reliable. A big are is the IoT, would it be great that the house can turn on the alarm or control the settings without of reconfiguring the parmas in some application, or it can detect weather conditions just pointing a camera to the sky and seal windows or put the roof over the terrace.
The main advantage of using AI is that software can be adapted to different runtime environments without actually writing code. According to Moore’s law, in the future, it will be a lot easier to provide resources for AI software. Also, it will give a lot of workplaces for AI engineers.
The Demand for Artificial Intelligence and Machine Learning Developers
The U.S. Bureau of Labor Statistics predicts that by 2026, the number of experts involved in AI and machine learning (across various industries) will reach 11.5M jobs, meaning the demand for experts will grow steadily. Traditionally, among the job titles that are requested in Ai-related job offers are:
- Data Scientist
- Software Engineer
- Machine Learning Engineer
- Software Architect
- Data Analyst
- Data Warehouse Engineer
- Full Stack Developers
- Research Scientist
- Front End Developer
- Product Manager
According to the LinkedIn 2018 Emerging Jobs Report, artificial intelligence and machine learning experts are reaching striking demand.
Machine learning engineers are in demand right now, and since 2014 the interest to and need in such experts has grown 12 times. Among the skills that are required most are: deep learning, machine learning, natural language processing, Tensorflow, Apache Spark.
Another interesting discovery is that machine learning specialists (a broader term that refers to experts of the industry) have 6-times growth over the same period of time. Dominating skills include Python, artificial intelligence, and deep learning.
Must-Have Skills in AI Programmer Resume
Before we discuss AI developer CV, let’s focus on what skills are needed and how they correlate with this technology? There is a great explanation by Drew Conway, which seems to be the perfect explanation for how data science differs from machine learning and what do the have in common.
However, even more questions emerge: where to begin? What specific skills does one need? What experience should a developer have?
What Do You Need to Work with AI/ML?
The answer is simple, people and hardware. Two kinds of developers are needed: data scientists and developers for building infrastructure.
Data scientists are the AI developers who provide the core of the algorithm, and the processing of raw incoming data, as well as putting it into the neural network. AI developer should have a math-related degree since most of data science frameworks require a good knowledge of it. Most of them use Python for development since it’s simple, and numerous frameworks like Pandas or Keras are written in Python.
The headliner of Machine learning is Tensorflow which is developed by Google. One of the requirements for Tensorflow is hardware since it’s can be executed using CUDA (running code using GPU), for production purposes a solution like NVIDIA DGX2 can be used. Such hardware cost a lot, but the data can be processed faster compared to the traditional datacenter machines that are using GPU.
Widely used machine learning platforms include Apache MXNet, TensorFlow, Caffe2, CNTK, SciKit-Learn, and Keras (and the overall machine learning engineer salary will be based on both their knowledge and experience).
Other developers/engineers, involved in the process, provide infrastructure to deploy huge amounts of data for ML solutions. Usually, the data providers are arranged as cloud solutions using AWS, Azure, DigitalOcean, etc. To reduce the cost of the solution runtime, it is good to have a DevOps that can optimize it to use fewer resources and to make it efficient. But even here Python experience is preferable since all the integration are made with Python-based software.
A good resume of AI developer should have about 4-5 year of experience for senior staff. If more years are listed in AI programmer resume, that’s probably not true, since earlier neural networks were used only in universities. They should be solid experience with Python frameworks and cloud platforms, and UNIX-based systems (since windows don’t have CUDA support). Another part of the CV should include more specific information whether this the data scientist or infrastructure developer (they will have different background).
List of Skills Required for a Machine Learning Engineer
- Exquisite math skills, in order to work with algorithms;
- Knowledge of Python, R, Java, and C++ (depending on the type of a project/domain);
- Hands-on experience with typed language (e.g. C++ and Java)
- Big data experience or knowledge: data preprocessing, SQL & NoSQL, ETL (extract, transform, load), data analysis, and visualization.
- Understanding of rapid prototyping;
- Based on the needs of a project, understanding of concepts like Auto-ML, voice and audio processing, language processing or robotic vision might be required.
Comparison of Machine Learning Developer Salary
The current situation was well described by Quartz as a ‘space-race redux’, where countries go above and beyond just to get a breakthrough. For this section, we have examined Payscale for skills like ‘artificial intelligence’ and ‘machine learning’ in profiles.
|Software Engineer (AI)||Software Engineer (ML)|
|Ukraine||from $24,000||from $26,697|
For easier comprehension and understanding, we’ve converted average salaries to USD and created visual data. We have gathered information for developers with ‘artificial intelligence’ and ‘machine learning’ among their skills. Let’s begin with the AI programming salary.
AI Engineer Salary
If we browse the same section for AI engineer salary, we will see that the United States dominates the market with the average annual AI developer salary of $101,034, when Germany offers $63,217 as average AI engineer salary. Canada provides median AI development salary of $58,790, and UK offers AI software engineer salary of $52,380. AI developer salary is defined by both skills and experience a developer can offer.
Such numbers make outsourcing seem like the best option, don’t they? Whereas in Ukraine such services can be found for as little as $24,000 of AI coder salary per annum. It means that compared to the US rates, a company can save more than $77K on AI engineer salary only.
Machine Learning Salary
Following the same approach, we examined annual salaries for engineers who have listed machine learning among their skills.
While discussing machine learning developer salary, we can see that Germany Canada tend to express more interest on developers with machine learning skills and experience.
These charts offer a perspective on average rates, however senior machine learning engineer salary can reach up to $135,000 according to PayScale reports.
For an overall comparison of machine learning developers by country, we will compare machine learning engineer salary with AI developer salary below:
However, we believe that this discussion would no be fair and honest if ignore the current job market offering. According to Indeed.com (and based on the vast number of opportunities listed), an average machine learning engineer salary in the USA scores $144,132 per year which is 28% more compared to reported average salary.
Does it mean that one of the sources is wrong? Not at all! Typically, such margins are the results of emerging interest in the technology, when official statistics is slower than the changes on the market (applicable to both AI engineer salary and machine learning engineer salary).
As more and more businesses implement machine learning and artificial intelligence into their business practices, the need for professional, qualified developers continues. Ukraine remains a leader in the IT industry with certified IT professionals. Mobilunity, a leader in outstaffing and outsourcing professional IT services, is one of the most trusted providers of quality development services in Ukraine. With 15,000 IT professionals graduating annually in Ukraine, Mobilunity is a popular choice of employment for highly skilled, top quality machine learning dedicated developers.