Machine Learning Engineer (ML Engineer) is the primary specialty in extensive systems development and Data Science. Demand for these specialists is on the rise! The number of relevant vacancies amounts to 38.54% of all industry vacancies. So, let’s figure out what is a Machine Learning Engineer and how to become one.
What Do You Mean By An ML Engineer?
ML Engineer is an expert in the field of artificial intelligence. He is the one who designs the algorithms deciding how the computer will think. We need ML to have neural networks that can analyze almost anything from game combinations to deep ad personalization on social media. ML can also be used as the basis for forecasting software that performs better than people.
This profession is a Data Science branch, and its activity focuses on practical tasks. ML Engineers solve business issues using ML algorithms. ML specialists also create chatbots that can interact with customers.
Necessary Skills for an ML Engineer
If you’re an aspiring ML Engineer, there are a few skills of utmost importance
The knowledge of algebra, applied statistics, and probability theory should be at the highest possible level. It’s normal to wonder what is a machine learning engineer and why math is an essential skill. ML Engineers often use Bayesian networks, Markov decision processes, hidden Markov models, and conditional probabilities. You also need to get into variance analysis and can test statistical hypotheses.
The programming language most often mentioned in vacancies description is Python. But employers also require knowledge of R, Java, C ++, Scala. Knowledge of such libraries like pandas, OpenCV, Numpy, Eigen, NLTK, Spacy, or scikit-learn is also necessary. Check out best reviewed books on C# here.
It is another basic skill you will need. The efficiency of ML that also increases the quality of ML decisions depends on data modeling skills. Primarily, you need to get into the modeling patterns, iterative learning, and evaluation strategy of the model accuracy.
Besides the knowledge of SQL, you will need skills in NoSQL DBMS.
Every international team requires English language knowledge, at least Intermediate level, but Upper-Intermediate would be much better.
Agile Project Management
It could be Agile, Scrum, Kanban, or other agile methodologies.
Additional Skills for Specialized Areas
- Natural Language Processing, Computer Linguistics, Sentiment Analysis for those who work with processing, understanding, and assessing text sentiment.
- Computer Vision for ML Engineers who work with image and video recognition.
- If you work with sound, sensory data, and other signals, Digital Signal Processing will be useful for you;
- Recommender Systems for their design.
- Analytical mind & logic
- Attention to details
Who Can Become an ML Specialist?
ML isn’t a beginner-friendly area. Once you’re aware of what a machine learning engineer is, you need to figure how to become one. You can get into this specialty from scratch. However, this requires at least strong math skills, ideally, higher education in math or economics. And even in this case, you should be prepared for challenges. For successful experience growth, you need to figure out how Data Science works.
Therefore, the best launchpad for future ML Specialists is a specialty of data scientists or data analysts. Python developers can also move to ML. To do this, you will need to figure out the main ML libraries.
An ML Engineer is a sub-specialty. In most cases, it is the final stage in these specialists’ careers. But if desired, they can always move to Data Analysis, Data Science, or Full Stack Development.
Advantages & Prospects
First, creating apps that go beyond standard programming is fascinating. This work uses your brains and forces you to do dozens of experiments, read scientific articles, and seek a solution to meet the target.
Secondly, ML allows companies and startups to create intelligent products and services. Such solutions solve problems that cannot be solved with conventional development. Today, even non-IT business gives careful attention to this field.
Today, ML is used in all areas of life. In particular, Internet users often see the results of the work of ML Engineers, although sometimes they are not even aware of this. For example, Pinterest is training its content recommendation system. Facebook has implemented ML to its social network, for example, chatbots and targeted advertising. Therefore, being aware of what is a machine learning engineer is quite important.
Large companies and their environment create vast amounts of data that can be used to get serious advantages. It led to the growing interest in Data Science and ML.