Every day artificial intelligence is entering our lives more and more. It isn't easy to imagine an area of life where digital technologies would not be involved. Their development requires more qualified specialists who can increase the level of artificial intelligence, bringing it closer to human thinking. Moreover, these should be not just specialists who know programming languages and the necessary algorithms but people with creative thinking who know how to find non-standard approaches to solving each problem. Therefore, the demand for machine learning engineers worldwide is difficult to overestimate.
Who is the Machine Learning Engineer
Machine learning is a branch of artificial intelligence. It is a data analysis technique that allows a machine, robot, or analytical system to learn independently by solving an array of similar tasks.
A machine learning engineer is an expert in engineered data and artificial intelligence. He develops new learning algorithms and also refines existing algorithms that regulate the activities of automated systems, computer programs, and web services.
Responsibilities of Machine Learning Specialist:
• Build machine learning models
• Train neural networks
• Use ML algorithms
• Work with data analysis tools (visualize data in Power BI and program in Python and SQL)
• Extract data from various sources
• Configure infrastructure
Current business niches for ML
Today, businesses have a stable need for machine learning engineers. Here are some niches:
Health. The priority area for ML in healthcare is increasing diagnostic accuracy. ML algorithms help develop the most justified and effective treatment plans. They do it not only for standard but also for rare diagnoses.
The most common scenarios for implementing machine learning in medicine are:
Reduced cost of treatment
Finding a link between symptoms and diseases
Prognosis of diagnosis
Financial industries. Approving loans and other banking products would take much longer without machine learning. The high level of security of banking transactions is also achieved using machine learning technologies.
The main advantage for the financial industry is the constant learning ability of algorithms. This ability is advantageous in preventing fraudulent transactions. Detecting credit and debit card fraud is one of the most promising areas for machine learning.
Machine learning helps identify credit risks, analyze the stock market, and segment customers.
Retail. It is one of the most comprehensive industries for machine learning capabilities. A typical machine learning model allows users to split large amounts of complex data into small quantities.
Thanks to this approach, users can study the target audience, predict the client's behavior and select the most relevant offers for him, make forecasts for the development and trends of the market, change the price, and assess future demand.
Transport industry. Carriers need ML to optimize logistics and reduce transportation costs. Machine learning is actively used in mobile applications to build optimal routes, train uncrewed vehicles, and regulate road traffic.
Algorithms of machine learning make the existence of self-driving cars possible. ML models allow cars to continuously collect data about their environment from cameras and other sensors, interpret them, and decide what actions to take.