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How To Become A Machine Learning Engineer

by Sneha Shukla

How To Become A Machine Learning Engineer


Machine learning is a swiftly evolving field that is critical to artificial intelligence development. Whenever you’re browsing Netflix film and television suggestions, you’re immediately dealing with the work of machine learning engineers who have been certified in a machine learning course. When you see adverts on social media that are related to your search history or interests, or speaking orders to Amazon’s Alexa or Apple’s Siri, you are dealing with a product from a machine learning engineer.

While becoming a machine learning engineer requires a lot of dedication, there is a gratifying payoff. Machine learning engineers are in a field that works at the vanguard of artificial intelligence and deep learning. It has a growing impact on the innovation and efficiency of industries ranging from retail to entertainment, finance, tech, healthcare, and auto. 

Who is a machine learning engineer?

A computer programmer that designs and constructs self-running software that get trained from data and automates prediction models is known as a machine learning engineer. Machine learning engineers are placed at the junction of data science and software engineering, possessing skills from both disciplines, due to the associative nature of the job, which requires both an understanding of data structures and data models as well as the capability to deploy those models in functional software. There are many machine learning courses available online.

As a result, machine learning engineers are very much in demand across all fields that rely heavily on automation, use large data, or are looking for methods to improve the efficiency of their services and systems. Machine Learning Course in London

What is the role of a machine learning engineer?

Engineers that specialise in machine learning, teach systems and software how to study on their own, without the need for human interaction. Consider how YouTube and Netflix’s recommendation algorithms work; Amazon’s purchase recommendations; and how social media applications and platforms may recognise spam or unsuitable content on their own.

In practise, this entails a slew of tasks, including doing machine learning experiments by means of programming languages, installing machine learning solutions, and tuning those solutions for scalability and performance, designing bespoke machine learning code; doing core data science tasks such as data analysis and use case generation; and executing foundational data engineering tasks such as ensuring a smooth flow between backend services and databases. 

Collaboration with other contributors, such as researchers and data scientists and, product managers, and software engineers, is an important element of the role, as it helps define roadmaps, project goals, and how each professional’s work can impact the work of others.

ML engineer job description

While machine learning engineer job descriptions will vary by industry, company, and team, a typical machine learning engineer job description will emphasise the requirement for someone who can develop and teach computers to study automatically.

A background in both software engineering and data science complements this skill set.

Proficiency in programming languages such as SQL, Python, and Java, as well as the ability to undertake hypothesis testing, data modelling, probability, and statistics, and arithmetic, are all expected talents in data science (such as the Hidden Markov Models, conditional probability, Naive Bayes classifiers, Bayes rule and Bayes nets, etc.), requires understanding of variance, likelihood, dynamic programming and correlations, and as well as the ability to devise a method for evaluating prediction models and machine learning algorithms.

To understand data structures such as graphs, stacks, queues, and multi-dimensional arrays; computability, and approximate algorithms; and awareness of computer structural design such as bandwidth, memory, clusters, deadlocks, and cache are all expected skills in software engineering. 

In six easy steps, you can learn how to be a machine learning engineer.

Machine learning engineering is a young and rapidly developing field. As a result, there is no one-size-fits-all approach to be a machine learning engineer. Depending on your technical talents, educational background, and areas of interest, there are a variety of ways to break into the profession. 

The steps outlined below can help you land a job as a machine learning engineer.

Recognize your ultimate goal. Before deciding whether to pursue a bachelor’s or master’s degree or enrol to an online bootcamp, you should have a clear idea of what you want to get from a career in machine learning engineering so you can choose the best path for you. Some machine learning jobs will demand a bachelor’s degree in computer science, statistics, mathematics, or a related discipline, while others will require a master’s or doctoral degree. Others will evaluate your qualifications based on your work experience and skill transferability.

Learn the fundamentals of software engineering. Machine learning engineers develop the code that runs systems and programmes, so they must be well-versed in a variety of programming languages (the most common being Java, Python, and C ) as well as fundamental computer science in order to create and deploy software.

Learn the principles of data science. Machine learning engineers have a lot in common with data scientists, which is one of the things that sets them separate from traditional software engineers. Anyone involved in machine learning engineering must know how to collect, clean, optimise, and query data sets, as well as grasp data models and connect data science findings with software engineering building blocks.

Learn how to use the tools and understand the ideas. It is beneficial to familiarise oneself with frequently used machine learning architecture and concepts in addition to learning programming languages. Spark, TensorFlow and Hadoop, Apache Kafka, R Programming, Weka, and MATLAB, for example, are likely to be used by machine learning experts working with AI and deep learning. Natural language processing, regression models, neural networks, and information retrieval are expected to be required skills for ML engineers working on virtual assistants or chatbots.

Participate in real-world initiatives. Understanding how to put in your theoretical knowledge to real-world activities and assignments is the most critical aspect of being a machine learning engineer. Finishing a machine learning engineering project from start to end and logging it in a portfolio will demonstrate to potential employers your ability to comprehend and deliver at each stage of the project. 

Take an online course or attend a bootcamp. While some ML engineers are able to complete these stages on their own, many others require assistance. As a result, candidates frequently go to an online bootcamp to learn ML engineering in a thorough and supported manner.

What does a machine learning engineer need to be aware of?

The first prerequisite is to have a solid understanding of data science and computer science, which includes knowing programming languages like Python, C , R, SQL, and Java, as well as tools like TensorFlow, MapReduce, and Spark. You can also take up a machine learning course to adapt yourself with adequate knowledge.

You should be familiar with mathematics, statistics, neural network architecture, data structures, signal processing techniques, memory management, and AI training ideas and applications.

Do machine learning engineers in demand

ML engineering provides a stable professional path with a wide range of prospects. You’ll need to learn how to code if you want to work in machine learning engineering. There’s no getting around it: many machine learning jobs necessitate a working knowledge of computer languages like Python, R, Javascript, or C.

While some machine learning ideas can be learned and understood without touching code, a machine learning engineer who wishes to use machine learning models to solve real-world issues will need a solid coding experience. Even basic programming expertise would open doors in machine learning since it will make graphical and scripting machine learning environments, such as Orange, Weka, and BigML, as well as ML libraries, accessible, allowing you to execute difficult tasks without writing a lot of code.

Is machine learning engineering a lucrative career?

Indeed named the profession the most promising in 2019 based on the amount of open positions and average compensation—a 344% increase in job posts from 2015 to 2018; an average base income of roughly $146,085—calling it a “very promising vocation.”

Machine learning engineering, in addition to employment stability, provides significant diversity and industry flexibility, as machine learning engineers are required in a wide range of industries, from healthcare and government to technology and entertainment, banking, and retail.

A bachelor’s or master’s degree in mathematics, computer science, statistics, or a related discipline is required for most machine learning engineering positions. However, whether or not a person is hired for a machine learning engineering position is largely determined by whether or not they have the necessary expertise, experience, and project portfolio to demonstrate that they can execute the job. 

A machine learning online certification course takes about six months to finish. It may take longer if the person has no prior experience in data science, computer programming, or statistics. There are many machine learning online certification courses available, from which you can utilise to gain domain knowledge.


Machine learning engineering is a well-paid, in-demand career with significant job growth and good remuneration. Because machine learning engineering is in such high demand across multiple industries—the profession saw a 344% increase in job listings from 2015 to 2018, and this number is expected to rise in the coming years as more organisations realise the potential of marrying big data with software—it offers strong career stability and a diverse range of opportunities

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