Machine learning is considered one of the hottest skills in technology. Everywhere organizations are putting a big bet on machine learning that “wirls” apps and services will provide more important benefits than competition. If you decide to become a machine learning engineer, you could unlock many fulfilling (and very advantageous) opportunities.
Artificial Intelligence (AI) and machine learning jobs have increased by almost 75% over the past four years. With the global machine learning market expected to reach $20.991 billion by 2029, it's no wonder that machine learning engineers who know their own can cut down their total pay ranges from $215,000 to $397,000 a year.
What is a Machine Learning Engineer?
Machine learning engineers are the role of hybrids, located at the intersection of data science and software engineering. They are primarily responsible for creating algorithms that use data to automate predictive models. Their goal is to allow computers to predict future events, apply what they have learned, and grow more intelligently over time.
To put that into perspective, data scientists analyze data to generate business insights, while machine learning engineers turn data into products, says Kurtis Pykes, a self-taught machine learning engineer. Isn't it interesting?
Find out the core responsibilities of ML engineers and the skills, qualities and experience required for their role. Responsibilities vary by organization, team, and project, but some general obligations are:
- ML models and systems research, design, and implementation
- Implementing machine learning algorithms and tools
- Scaling Data Science Prototypes
- Select the right dataset, check the quality of your data, clean and organize your data (cooperate with data engineers)
- Performing statistical analysis
- Test execution and machine learning models and algorithm optimization
- Production monitoring systems and retrain them to improve performance
- Use the machine learning library
Technical Skills and Knowledge Requirements
Because machine learning engineering is not an entry-level role, most technology professionals moving to IT have experience in software engineering or degrees in computer science, engineering, mathematics, or data science. Companies looking to build data science teams are opening up new pathways to becoming machine learning engineers, including self-taught options.
Whether you want to expand and jump your current skill set or start from scratch, here are the technical skills you need to master:
programming: Python and R are the most popular languages for machine learning practitioners. However, some companies may need to know about C++, JavaScript, Java. Below is a list of free R programming courses as well as a list of free and paid courses to learn Python, SQL, data science and other data analytics skills online.
Mathematics, Probability, Statistics: If you don't have a CS or mathematics degree or need a review, an online course is probably the best way to learn the basics.
Machine Learning Algorithms and Frameworks: Pikes said it is doubtful that machine learning algorithms need to be implemented from scratch. However, to recognize the advantages, disadvantages and trade-offs when selecting the right model and optimizing it for your task, you need to have a proper knowledge of machine learning algorithms, hyperparameters, and how each hyperparameter affects learning.
Software Engineering and System Design: Machine learning engineers need to understand the various software engineering best practices (i.e. version control, tests, documentation, modular coding, etc.) and how different pieces form a system.
mlops: Machine Learning Operations (MLOPS) is one of the core functions of machine learning engineering. To learn MLOPS, Pykes recommends studying lessons featured in ML and reading “MLOPS: How to Expand Machine Learning in Enterprise” and “Practical MLOPS: Operational Machine Learning Models.”
Soft skills are important
Machine learning engineering is a technical job, but soft skills are essential for predicting success and can have a positive impact on your career. Here are some of the most important things:
Communication and teamwork: Machine learning engineers often act as part of large data science and/or cross-functional teams, so the ability to communicate and collaborate information to technical and non-technical audiences is a critical skill.
Problem Solved: Naturally, one of the most important skills an engineer needs to bring to the table is his ability to think critically and solve problems. However, transitioners should note that machine learning research is different from actually doing machine learning. To hone your skills, use the entire workflow to solve real problems without using fixed datasets.
Time Management: ML engineers often need to research, plan, and execute multiple projects, meeting the needs of multiple stakeholders simultaneously.
Continuous Learners: Staying up to date in this rapidly evolving field requires tips to quickly learn new tools, how they work, where they work well, and where they don't.
Gain abilities and confidence
When it comes to getting their first job, two things work for them. First, despite the lack of supply of qualified candidates, hiring managers want to see the proficiency of the most important skill sets and tools before extending the offer. Second, newcomers may experience a lack of confidence or self-doubt about their abilities (commonly referred to as fraud syndrome).
What is the solution? Gaining practical experience and building a portfolio while studying can not only increase your abilities and confidence, but also increase your chances of being hired. Don't wait to practice your skills, Pikes said. For example, you will continuously apply newly acquired skills through project-based learning.
There are basic machine learning projects for beginners who are skilled in R or Python, and projects suitable for those with intermediate and advanced machine learning skills. Blogging about your projects can help you understand the nuances behind your job, improve your communication skills, and create valuable connections with recruiters and hiring managers.
Another option is to participate in data science competitions such as DataCamp and Kaggle. Contributing to this kind of competition is highly regarded among many employers and serves as a great way to build a portfolio. You will learn what it's like to compete with this Kaggle Competition tutorial.
There is a sharp learning curve to become a machine learning engineer, but the rewards are definitely worth it. Machine learning will become more important to organizations everywhere for the next few years.
Related Machine Learning Engineer Employment Resources:
Machine Learning Engineer Interview Questions
