Machine Learning Engineer Career Guide (2024) | by Careervira | Medium

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In this blog, we will outline key fundamentals you need to know about the Machine Learning Engineer career path to help you make informed decisions about your future.

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Are you considering a career as a Machine Learning Engineer? With the increasing demand for professionals in the field of artificial intelligence and data science, the role of a Machine Learning Engineer has become more prominent than ever. However, before you jump into this career path, there are a few key things you need to know.

If you are interested in pursuing a career as a Machine Learning Engineer, it is important to understand the skills, education, and job prospects associated with this field. In this blog, we will outline key fundamentals you need to know about the ML Engineer career path to help you make informed decisions about your future.

Let’s start with exploring the basic part of who these professionals are.

What is a Machine Learning Engineer?

A Machine Learning Engineer is a programmer who builds software systems that use artificial intelligence and machine learning. They take vast amounts of data and use it to train algorithms to perform specific tasks, like making predictions or classifications. In other words, ML engineers are like the bridge between the world of data science and the real world, where they take the insights extracted from data and turn them into practical applications.

How to become a Machine Learning Engineer?

Here’s an easy breakdown of the roadmap you must follow, to become an ML engineer:

1. Education:

• Formal Education: Pursue a bachelor’s degree in a related field such as computer science, mathematics, statistics, or engineering. A master’s or Ph.D. can provide a competitive edge in the field.
• Alternative Approaches: If you already have a degree in a different field, consider online machine learning courses or certifications that equip you with fundamentals including a foundation in math, particularly linear algebra, calculus, and probability & statistics. These are crucial for grasping machine learning algorithms.

2. Build Technical Skills:

• Master Python Programming: It is important to be proficient in Python, the dominant language in Machine Learning. Focus on mastering its syntax, data structures, and object-oriented programming (OOP) concepts.
• Libraries: Explore popular machine learning libraries like TensorFlow, PyTorch, and scikit-learn. These libraries provide pre-built functions and tools for various machine-learning tasks.
• Other Languages: Consider learning R or Java. While not essential, familiarity with these languages can give you an edge in specific areas of Machine Learning or Big Data.
• Linear Algebra: This branch of math is crucial for understanding how machine learning algorithms represent and manipulate data. Key concepts include eigenvalues, eigenvectors, and matrix operations.
• Calculus: Derivatives and integrals play a vital role in optimizing machine learning models. This is one of the essential math skills.
• Probability & Statistics: Probability theory helps understand uncertainty in data, while statistics provides tools to analyze and interpret data patterns. Understanding concepts like hypothesis testing, correlation, and random variables is essential.
• Data Cleaning and Manipulation: Learn tools like pandas for Python to clean, organize, and manipulate large datasets.
• Data Visualization: Utilize libraries like Matplotlib and Seaborn to create informative visualizations that help you explore and understand your data.
• Version Control: Using Git for version control helps you track changes in your code and collaborate effectively with others.
• Cloud Platforms: Gaining experience with cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure can be helpful for deploying and managing machine learning models in production.

3. Learn with Online Courses:

• IBM Machine Learning Professional Certificate by Coursera: This intermediate-level course emphasizes hands-on learning through labs and projects, equipping you with skills applicable to real-world machine-learning tasks. It will help you gain experience with industry-standard tools and libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and Keras.
• AWS Machine Learning Engineer by Udacity: This advanced-level course teaches you how to build predictive models using a variety of unsupervised and supervised machine-learning techniques.
• Machine Learning 101 by Guvi: This three-month course is great for learning how to build and tune predictive classification and regression models and how they compare with real-world data.
• Machine Learning Fundamentals with Python by DataCamp: This course is an excellent choice for beginners looking to enter the world of machine learning. It provides a strong foundation through a well-structured curriculum and hands-on exercises.
• Intro to Machine Learning with PyTorch by Udacity: This three-month program emphasizes hands-on learning through projects where you’ll build and deploy real-world machine learning models using PyTorch.

4. Practical Experience:

• Personal Projects: Work on independent machine learning projects to solidify your knowledge. Find a problem you’re interested in that can be solved with machine learning, and build a project around it. There are many online resources and datasets available to get you started. Document your project clearly, including the problem you aimed to solve, the data you used, the algorithms you implemented, and the results you achieved.
• Open Source Contribution: Look for opportunities to contribute to open-source machine learning projects. You can also consider participating in Kaggle competitions to showcase your abilities. Start by looking for beginner-friendly projects and gradually progress to more complex contributions as you build your skills.

5. Build a Strong Portfolio:

• Portfolio: Showcase your projects, code, and results. A well-documented portfolio provides evidence of your skills and practical experience. Include links to GitHub repositories, Kaggle profiles, or personal websites where employers can review your work.

6. Gain Work Experience:

• Freelance Work: Look for freelance opportunities on platforms like Upwork or Kaggle that involve machine learning tasks. This can provide practical experience working on client projects.
• Internships: Machine Learning Engineer internships are a great way to gain industry experience. These internships often involve working on real-world projects under the guidance of experienced professionals.
• Entry-Level Jobs: While not directly Machine Learning Engineer roles, consider entry-level jobs in data science, software engineering, or analytics. These roles can provide valuable experience working with data, programming, and problem-solving, which are all transferable skills for Machine Learning Engineering.

7. Networking:

• Online Forums: Create a strong LinkedIn profile and join machine learning & AI-related groups to connect with professionals in the field.
• Professional Events: Attend conferences, workshops, and meetups related to machine learning and artificial intelligence to meet professionals and potential mentors.

What does a Machine Learning Engineer do?

Some of the responsibilities generally mentioned in the machine learning engineer job description are as follows:

• Design and develop machine learning systems: This involves taking complex algorithms and making them work in the real world. The machine learning engineers role involves considering things like how much data the system can handle, how it will be deployed, and how it will interact with users.
• Implement machine learning algorithms: Machine learning engineers use programming languages like Python and libraries like TensorFlow to write the code that makes the algorithms work.
• Run experiments and tests: Machine learning engineers design and run tests to check the accuracy of the models and identify areas for improvement.
• Maintain and improve existing systems: Machine learning engineers monitor the performance of these systems and make changes to improve their accuracy and efficiency.
• Collaboration: They collaborate with data scientists who develop the algorithms, software engineers who integrate the models into larger systems, and domain experts who provide problem-specific knowledge.

How Much Does A Machine Learning Engineer Make?

Here is a quick breakdown of the salary offered to machine learning engineers:

US:

• The average annual salary offered to entry-level machine learning engineers with up to 3 years of experience is $93,000.
• The average annual salary offered to mid-level machine learning engineers with up to 6 years of experience is $115,000.
• The average annual salary offered to late-level machine learning engineers with up to 9 years of experience is $138,000.
• The average annual salary offered to experienced-level machine learning engineers with more than 9 years of experience is $157,000.

India:

• The average annual machine learning engineer salary at entry level with up to 3 years of experience is ₹7,25,000.
• The average annual salary for machine learning engineers at mid-level with up to 6 years of experience is ₹15,00,000.
• The average annual salary for machine learning engineers at late level with up to 9 years of experience is ₹20,00,000.

What’s Next?

There are plenty of machine learning engineer jobs available; you just need to have the right skills and the ability to grasp the opportunity. If you want to stand out from the competition, take the help of Careervira. You can explore all the top premium ML courses, compare them, and pick the one that is right for you. So what are you waiting for? Start your learning journey now.