Machine learning (ML) is a subfield of artificial intelligence (AI) and computer science that focuses on imitating how humans learn by leveraging data and algorithms. The main objective of machine learning is to identify patterns in data. It’s widely believed that AI will continue to transform business as we know it, and this revolution is well underway across various industries. Here are pros and cons of machine learning engineering.
Who is ML Engineer?
Since Machine Learning is deeply linked with Data Science, there are some overlaps in the job description of a Data Scientist, Data Analyst, or a Machine Learning Engineer. However, the main differences are what they focus on. Data Scientists and Analysts are mainly focused on obtaining insights from the data and presenting them to the higher-ups in the organization to make important decisions. This involves having some knowledge of ML algorithms. However, Machine Learning Engineers are excluding focused on Machine Learning. Their aim is to create software components that can work with minimal human supervision and help in obtaining insights from the data they are provided with. This is why there is so much focus on Computer fundamentals and software development for a Machine Learning Engineer because that is their specialization
Recognise ML Concepts
Familiarize yourself with various machine learning algorithms, including linear regression, decision trees, support vector machines, and neural networks. Understanding how these algorithms work and when to use them is essential. Gain hands-on experience with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. These tools simplify the implementation of complex algorithms and models. Practice by building and experimenting with models on platforms.
Pursue Software Engineering
Learn the principles of designing scalable and efficient systems. This includes understanding APIs, microservices, and cloud computing. Resources like “Designing Data-Intensive Applications” by Martin Kleppmann provide in-depth knowledge of system design. Master version control systems like Git and platforms. These are essential for collaboration and managing codebases effectively. Participate in open-source projects to gain real-world experience.
Build a Portfolio
Work on real-world projects that showcase your skills. These can range from simple data analysis tasks to complex machine learning applications. Document your projects thoroughly and publish them on platforms like GitHub or personal blogs. These competitions provide practical experience and help you apply your knowledge to solve real-world problems.
Salary
It’s clear to see that this may need some refining. Since many companies are now accepting more and more remote workers. There has been an ongoing debate on how to pay employees fairly given the rise of remote work. Some companies have resolved to pay employees based on their location, which means you could earn less than someone in the same role as you if you’re working from a lesser economically developed country and they’re in office.
The outcome of a machine learning engineering workflow is a data product. To work effectively as a machine learning engineer, you must be a technically sound programmer with a solid math, statistics, probability, and software engineering foundation. Although it’s often requested in job descriptions, a degree is generally not required for most companies, but it is necessary to demonstrate your capabilities with a portfolio.
