Machine learning is a powerful force that has the potential to transform machines and their utility. It is a subset of artificial intelligence that aims to simulate human intelligence in machines. The growing popularity of machine learning and the demand for trained professionals in this field have led to an increase in machine learning engineer jobs. These jobs offer a wide range of career benefits and are highly valuable in the world of data. Machine learning engineers are capable of creating end-to-end solutions and possess a set of highly marketable skills. This job title has seen rapid growth since 2019.
For beginners aspiring to become machine learning engineers, the task of learning versatile skills may seem overwhelming. However, there is a roadmap that can help guide your career in this field. In order to become an ML engineer, you need more than just knowledge of a specific programming language. Let’s explore the important steps you need to follow in order to become an ML engineer.
Before creating a roadmap, it is important to understand why you should pursue a career in machine learning engineering. Machine learning is already a part of our everyday lives through various digital services such as recommendation systems, voice assistants, and social media feeds. Machine learning engineers are responsible for developing the technology behind these services and ensuring their flawless functionality. The demand for professionals with machine learning skills is increasing as new models need to be developed and deployed. Glassdoor ranked ML engineer as the 6th position in the Top 50 Jobs in America in 2022, and there is expected to be a significant increase in job opportunities in this field. The World Economic Forum predicts that AI, automation, and machine learning will create almost 97 million jobs by 2025, and Forrester estimates that the business value generated by AI and machine learning could reach $37 billion by 2025.
The roles and responsibilities of machine learning engineers involve programming skills and the ability to create AI systems using ML models. They are an integral part of the data science team and are responsible for research, design, and development of ML models. They also monitor and optimize ML processes, evaluate and organize data, and execute tests and experiments. Collaboration with software engineers and data scientists is essential in order to create efficient and scalable ML solutions.
To become a machine learning engineer, you need a broad set of skills. The average salary for ML engineers in the US is $148,485, and experienced engineers can earn even more. Here is a step-by-step roadmap for building your career as a machine learning engineer:
1. Familiarize yourself with computer science and programming fundamentals. Programming languages such as Python, C++, Scala, Java, and R are essential in machine learning. Python is currently the most preferred language in this field.
2. Choose an operating system. Linux is the most important tool in the machine learning ecosystem, so it is important to learn how to use it, alongside other important Python packages for ML.
3. Learn the concepts of statistics and probability. Machine learning algorithms are often created by extending statistical modeling procedures, so a solid understanding of probability and statistics is necessary.
4. Specialize in machine learning algorithms. Learn about popular machine learning libraries, packages, and APIs such as TensorFlow, Spark MLib, Theano, Keras, Torch, and Sci-Kit Learn. Understand how to choose the right model and learning methods, and learn about hyperparameter tuning.
5. Learn data processing, modeling, and ML model evaluation. Understand how to preprocess data, build ML models, and evaluate their performance.
By following this roadmap and continuously learning and improving your skills, you can build a successful career as a machine learning engineer.
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