A Step By Step Guide to Become a Machine Learning Engineer

Machine learning is a powerful force with the potential to transform the meaning and utility of machines around us. It is a subset of artificial intelligence aimed at simulation of human intelligence in machines. The exponential growth in popularity of machine learning use cases and mainstream attention to ML has created a demand for trained ML professionals.One of the most notable entries among career opportunities in machine learning points at machine learning engineer jobs, which offer a broad range of career benefits. As a matter of fact, machine learning engineers are the most invaluable assets in the world of data. ML engineers are capable of creating end-to-end ML solutions, thereby showcasing a highly marketable set of skills. Most important of all, machine engineering has been one of the fastest-growing job titles since 2019.

Beginners aspiring to become machine learning engineer are likely to feel overwhelmed with the daunting task of learning versatile skills. If you want to pursue a career in ML engineering, then you need more than knowledge of a specific programming language. However, you can find a solution in the form of a roadmap for your career as a machine learning engineer. Let us explore the important steps you need to follow to become an ML engineer in the following discussion.

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Why Should You Pursue a Career in Machine Learning Engineering?

Before creating a machine learning engineer roadmap, you should clear your doubts about switching careers to machine learning. Machine learning is an important part of our everyday lives in the form of different digital services. For example, the recommendation systems on YouTube and Netflix use machine learning.

In addition, machine learning also empowers voice assistants such as Alexa and Siri alongside the functionalities of social media feeds on Twitter and Facebook. Machine learning utilizes statistics to discover patterns in huge repositories of data available across different digital platforms and devices.

The technology behind the ML-powered digital services is developed by machine learning engineers and data science projects. In addition, machine learning engineers have to ensure maintenance of machine learning applications to ensure flawless functionalities. The demand for professionals with machine learning engineer skills has been increasing due to the need for developing and deploying new machine learning models. Machine learning engineer is a popular job title in the IT labor market, and it will gain more traction in the future.

Glassdoor added ML engineer at the 6th position in the Top 50 Jobs in America in 2022. It also reported the introduction of 6801 new listings for machine learning jobs. According to the World Economic Forum, AI, automation, and machine learning will create almost 97 million jobs by 2025. Forrester has claimed that the business value generated by AI and machine learning could be almost $37 billion by 2025. Therefore, it is reasonable to assume that machine learning engineers could find easier access to lucrative job opportunities, even in unstable markets.

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Roles and Responsibilities of Machine Learning Engineers

Machine learning engineers have skills in programming languages for creating AI systems, which could use ML models for learning from large datasets. Most of the listings for machine learning engineer jobs demand skills in programming languages such as Java, C, C++, and Python. Machine learning engineers are a crucial part of the data science team, and their primary responsibilities focus on research, design, and development of ML models.

Some of the other notable responsibilities of ML engineers include monitoring and optimization of ML processes and evaluation and organization of data. In addition, the job role of machine learning engineers also involves execution of tests and experiments. On top of it, ML engineers must also work in collaboration with software engineers and data scientists to create efficient and scalable ML solutions.

Here is an outline of the important tasks in the job description of machine learning engineers.

  • Implementation of machine learning and statistical analysis for high-performance production systems with higher availability to offer better accessibility to users.
  • Improvement of machine learning libraries and frameworks.
  • Training and re-training machine learning systems.
  • Automation of feature engineering, evaluation, and the model training process.
  • Translation of machine learning models from environments such as Python and R notebooks into analytic applications.
  • Development of APIs or web services that could serve model outcomes for internal teams, users, or stakeholders.

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How Can You Become a Machine Learning Engineer?

The outline of roles and responsibilities of machine learning engineers shows that you would need a broad set of skills. Interestingly, you could also expect a promising reward in the form of financial benefits. The average machine learning engineer salary estimate in the US is $148,485. On top of it, experienced machine learning engineers could earn around $200,000 in larger markets.

For example, ML engineers in San Francisco could earn more than $200,000 in their jobs. However, it still does not deny the fact that you would have to invest a lot of effort and dedication in learning the important skills required for becoming an ML engineer. Here is a step-by-step roadmap for building your career as a machine learning engineer.

  • Familiarize with Computer Science and Programming Fundamentals

Programming languages are the core components in the domain of machine learning. Machine learning engineers have to rely on programming languages for developing machine learning algorithms. Some of the notable programming languages included in a list of machine learning engineer skills include Python, C++, Scala, Java, and R.

The good news is that you can learn any programming language for a career in machine learning, and you can do it within a few months. As of now, Python is the most preferred programming language in machine learning. Python offers the advantage of simple syntax and comprehensible code. Here are the important aspects you must cover while learning any programming language.

  1. Develop the skills for creating specialized data structures such as prefix trees, binary trees, or linked lists.
  2. Exception handling.
  3. Ability to work with data structures such as dictionaries, lists, sets, and maps.
  4. Hands-on experience in selection of the ideal data structure.
  5. Learn about the applications of vectorized operations instead of using loops.

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  • Choose an Operating System

The next step in preparing for machine learning engineer jobs is the ability to use Linux. It is the most important tool in the machine learning ecosystem and a formidable competitor to Mac and Windows. However, professional machine learning engineers must have skills in installation of Linux alongside other important Python packages for ML. In addition, you must learn the methods for working with the Linux file system or transferring data from Linux OS. Linux is one of the most powerful tools in the arsenal of a machine learning engineer, guaranteeing speed and flexibility.

  • Learn the Concepts of Statistics and Probability

The job description of machine learning engineers also entails the use of concepts related to probability and statistics. Candidates aspiring to become machine learning engineer must remember that majority of machine learning algorithms are created through extending statistical modeling procedures. Therefore, machine learning engineers must learn about the fundamental concepts associated with probability and statistics. Some of the concepts include conditional probability, hypothesis testing, types of distribution, hidden Markov models, and Bayes Net.

  • Specialize in Machine Learning Algorithms

The most crucial addition among the skills required to become a machine learning engineer points to expertise in ML algorithms. You must learn about machine learning libraries, packages, and APIs such as TensorFlow, Spark MLib, Theano, Keras, Torch, Sci-Kit Learn, and others.

These tools are an important highlight in every machine learning engineer roadmap as they provide standard implementations for ML algorithms. On top of it, you must also learn how to choose the right model and learning methods. In addition, you must also learn about hyperparameter tuning to understand the influence of parameters on the algorithms learning process.

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  • Learn Data Processing, Modelling, and ML Model Performance Evaluation

Machine learning engineers have to train the ML models to achieve high performance using the dataset structure. ML engineers must also know the best practices for choosing the ideal evaluation strategy. In addition, you have to learn the important error metrics for machine learning models.

  • Register for Professional Machine Learning Training Courses

The importance of professional training in career development can never be undermined. You can learn machine learning engineer skills in a formal way. Aspiring candidates can choose online training courses, machine learning certification programs, or boot camps to validate their knowledge of machine learning.

Learners should choose credible training programs for an in-depth understanding of machine learning within a few months. Training courses could help you learn about the applications of machine learning algorithms in real-world business issues. In addition, the training programs also help in developing practical expertise by learning from demo algorithms.

You could learn how to choose the right machine-learning model and use real data for developing machine-learning projects. Furthermore, training courses also help in specializing in the uses of machine learning frameworks and libraries alongside the best practices for making decisions.

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  • Work On Your Own Machine Learning Project

The dreams of earning a machine learning engineer salary package will only come closer with formal training and certification. However, you have to make the additional effort to reach out and fulfilling the dream of becoming an ML engineer. In the initial stages, you could review and copy the basic examples available on Sci-Kit Learn. Gradually, you will develop a comprehensive impression of the working of machine learning in practice. Subsequently, you can apply your innovative ideas and create unique projects that could serve as prominent highlights on your resume.

The ideal approach for choosing a machine learning project involves selection of simple algorithms and projects according to your interests. You would come across the challenge of a learning curve while building the algorithm from scratch. However, it would help you earn machine learning engineer skills that could offer long-term benefits. Beginners should also use publicly available datasets to avoid the trouble of wasting time in data collection. If you fall short of ideas to create your machine learning project, then Github is always there to the rescue.

  • Participate in Machine Learning Communities

The journey of finding a machine learning engineer job starts with professional networking. The first step in professional networking involves creating an appealing resume. Some of the most important highlights in the resume of an ML engineer include their academic qualifications, machine learning skills, and projects they have worked on. You can become machine learning engineer when you tick all the boxes required by employers seeking machine learning professionals. One of the most promising platforms for marketing your skills is online machine learning communities.

Kaggle is one of the popular online communities for machine learning professionals and data scientists. It helps users in searching and publishing datasets alongside building models in a web-based environment. Most important of all, Kaggle provides the opportunity to interact with other machine learning engineers.

Online communities could also provide the benefit of improving your knowledge of machine learning by learning from other professionals. You can also find a wide assortment of challenges based on machine learning with monetary rewards. Learners can participate in competitions to earn experience required to improve their resumes.

  • Apply for Internships and Jobs

Job search is a significant part of defining your machine learning engineer roadmap, as it requires a lot of effort. You should showcase your abilities to deliver important value advantages to businesses interested in adopting machine learning solutions. Entry-level internships can serve as a promising choice to start your career in ML and build your skills for advanced roles in ML engineering.

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Bottom Line

The guide to pursuing your career as a machine learning engineer provided an outline of important steps for achieving your objectives. As the number of machine learning engineer jobs continues to increase with each passing year, more candidates will search for guides and roadmaps. Machine learning is still a new concept, and it is evolving continuously.

Therefore, businesses using ML solutions would need professionals with versatile skillsets in ML, such as choosing the right data and training models. You would have to learn about programming and computer science fundamentals, statistics and probability, and machine learning algorithms. Start learning more about ML fundamentals and prepare to become a machine learning engineer now.

This article was originally reported on 101 Blockchains.