A Day in the Life of a Machine Learning Engineer

We aspirants often wonder what could be the life of a machine learning engineer. We also ponder what ML projects they might have to work on for the organization. Yes, the life of machine learning engineers revolves around artificial intelligence and machine learning project development. This article will give you a comprehensive idea of what machine learning engineers do and what skills and qualifications they need to create such impressive ML projects. You can learn more about Machine learning at ProjectPro machine learning project.

What is Machine Learning (ML)?

Machine learning is a sub-domain of Artificial Intelligence (AI) where the ML engineers provide them the ability to learn on their own without being explicitly programmed. In such a system, the algorithm or the model automatically learns and improves from the experience it receives. These algorithms also use a training dataset to enable the system to learn on its own.

Who are Machine learning engineers?

Machine learning engineers are computer engineers who create projects on machine learning and artificial intelligence. In other words & in the most basic sense, they are computer programmers, focusing on developing AI models that can self-learn from experience or datasets. A typical machine learning engineer will work on various applications of machine learning, such as self-driving cars, Natural Language Processing assistants, chatbots, etc. According to Semrush's report, by 2025, the demand for there will be 97 million job vacancies for AI and machine learning specialists, process automation specialists, and other related domains.

What basic qualifications a machine learning engineer needs to work in an organization on various machine learning projects?

Joining an organization as a machine learning engineer requires some basic qualifications. An educational background in computer science or mathematics is what an employer looks for when hiring a machine learning engineer. A master's degree or doctoral degree in these disciplines becomes an ideal choice for the organization to hire a candidate. Apart from being well-versed in programming, understanding datasets, data structures, and algorithms development are also crucial.

Skills required for Machine learning engineers –

Various skills that an ML engineer should have to get into an organization are:

Hard skills –

• Fundamentals of programming

• Understanding of statistics and probability 

• Data evaluation and modeling

• Working with machine learning libraries and models

• Experience in software engineering and system design

• Exploration and data visualization abilities

Soft skills –

• Writing skills

• Problem-solving skills

• Communication skills with fellow teammates

A Day In the Life of a Machine Learning Engineer

Let's assume an ML engineer starts the day at 9 AM. For the first one hour, the ML engineer catches up on the project part left previously. If it's a new project, the engineer will do quick research & analysis of the requirement gathering. He/she also checks the email and to-do list to make sure what he/she should work on today.

For the next two hours, that is, from 10 AM to 12 noon, the engineer will prefer to finish meetings and other work-related calls. During this time, the engineer can also make it productive by discussing a few pointers with seniors & fellow colleagues or learning new tools like Scikit Learn, TensorFlow, etc., or creative ML techniques in between meetings or extending the slab to 12:30 PM.

Post lunch, i.e., after 1 PM or so, the engineer will take care of office meetings (if remains) & client calls to discuss the project ideas and progress of the ongoing projects. After a half an hour to 1 hour of discussion, the engineer will start developing various modules and will keep executing it to test the training model. Once the training of model gets completed. The engineer will check the different metrics of the existing models & compare these with the model baseline. He then resumes the coding & finally reviews the requests the client highlighted in the previous discussion. 

Between 6 PM to 8 PM, the engineer will wrap up everything, such as opened tabs in browsers, datasets, training models in the IDE, etc., and finally, shut down the system. After heading home & finishing dinner, the engineer might try going through any work-related issues. In case, anything pops up, he/she will solve it if the project demands immediate action. The engineer can also try looking for solutions online.

We hope this article has given a comprehensive idea of how the entire day goes as a machine learning engineer. An ML engineer needs to understand the complete ecosystem for which he/she is developing the ML project. The limitless applications of machine learning are the reason for generating more job vacancies for ML engineers. The craze to become machine learning engineers' increases when one could imagine - in almost every field, organizations can apply machine learning concepts.