Machine learning engineering is becoming more and more popular across the IT industry. There are certainly many questions to address if you are interested in pursuing a career in machine learning. Only recently, Identrics ML engineer, Kristian Krastev, shared all the information you might need before delving into the depths of this specific field. He is currently working on developing natural language processing (NLP) software using machine learning algorithms.

In this blog we answer the most common “Whats” about pursuing a career in Machine Learning.

What is machine learning

Machine learning is a branch of artificial intelligence, which allows machines to use mathematical algorithms to receive data and “learn” to improve themselves without coding. We use machine learning in various industries – from financial analysis to categorizing objects.

Machine-learning algorithms find patterns in any amount of data and apply them depending on the given task. Machine learning is behind almost every service we use today – from Netflix’s movie and YouTube’s video recommendation systems to smart voice assistants like Alexa.

As far as the industry goes, machine learning is becoming one of the most desired fields to work in. Machine learning engineers are in high demand and apparently this trend will continue in the near future.

What does a machine learning engineer do

Machine Learning Engineers build and design software. That software can generate and develop algorithms, which are capable of learning and making predictions. The specific duties of a machine learning engineer will largely depend on the project they’re currently working on. Still, most machine learning engineers work on designing and developing software models.

Another ML engineer’s responsibility is to perform statistical analysis and use the results to improve the systems. The machine learning systems need to be automatized and efficient because the engineer spends a lot of time experimenting with the ML algorithms. At Identrics, the projects focus mainly on natural language processing. 

Kristian Krastev, Identrics’ machine learning engineer, works on applying AI to textual data and designing software architectures. His role covers the entire process – from input processing to selecting and setting up the correct models to solving a particular task and delivering the finished product to the customer.

What major challenges do machine learning engineers face

Every career path has its own set of challenges. For a machine learning engineer, one such challenge may be the balancing between research, development, and building software infrastructure. Other obstacles to machine learning engineering can include dealing with improperly formatted or incomplete data, or reproducibility.

Reproducibility is the ability to reproduce the results from a specific machine learning model again and again. That’s a huge challenge for every machine learning engineer. Another obstacle ML engineers might face is monitoring how the machine learning model works. Monitoring will ensure that the model is performing correctly.

What qualifications do machine learning engineers have

Machine learning engineers need to have qualifications to make sure they would do the job well. Knowledge of mathematics, software engineering, and statistics is usually required. The most common programming languages in machine learning are Python, R, and C++.

Most of the requirements for machine learning engineers include a degree in data science, computer science, and computer programming. However, a machine learning engineer is not an entry-level position. For this reason it is highly recommended to have some work experience in software engineering, software programming, or software development.

Usually, machine learning engineers will be required to have previous experience in ML platforms and machine learning programming languages such as Python, C++, JavaScript, or similar. Expertise with data modeling and statistics is also a huge advantage. Our ML engineer on feature, for example, began his career path as a computer programmer.

Check out our Internship Stories for more details about working at Identrics from an intern’s POV.

Is machine learning engineering a good fit for everyone

While anyone with experience and ambition can become a machine learning engineer, the most important part of the job is the desire to learn. The technology industry is advancing so quickly that keeping up to date with the latest trends is necessary.

Our ML engineer, Kristian also recommends that people who want to develop their careers in machine learning engineering should have advanced critical thinking and understanding of each task they have on hand. He also says that qualities such as teamwork, time management, and software design approach are crucial.

Kristian’s biggest advice for people interested in machine learning is to find a particular area of the field that attracts them and focus most attention on. That will ultimately open up a better prospect and increase the value of your work.   

Machine learning engineering is about constant learning

Kristian Krastev

The AI industry is constantly evolving, and a career in machine learning is becoming one of the most demanded in the years ahead. With technology changing so fast, it’s important that specialists also take a step to learn as much as they can. As Kristian puts it, one of the most rewarding parts of that career is the opportunity to face challenges and create a solution yourself. 

Kristian Krastev, ML Engineer at Identrics

Kristian is an R&D engineer and data scientist, working on Machine/Deep Learning applications for Natural Language Processing in the data intelligence industry. He is skilled in Python, Java and C/C++. A cinema buff, Kristian volunteers at a local film production company, and speaks Russian and French.

You can get additional valuable advices by Kristian (our NLP hero) in the full article published by Capital’s Careers magazine here.

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