top of page

How To Become A Machine Learning Engineer?

Updated: Nov 30, 2020

Many of my friends from computer science background ask me questions like, how to become a Machine learning engineer in India, how much does a Machine learning Engineer earn, or how can I become a Machine Learning engineer without a college degree. So, I thought why not write blogs on these topics. So, let's get started. I will be sharing some easy and proven methods with which someone can get started with Machine learning. So, we will go there but first, let's discuss some basics things.

How To Become A Machine Learning Engineer
How To Become A Machine Learning Engineer

Why become a Machine learning engineer?

Just because artificial intelligence and machine learning are trending and booming, doesn't mean that one has to get started with these topics. I believe everyone should focus on what they do best. So, the first exercise one should do is to ask himself or herself about Why I want to become a Machine Learning engineer?.

If your answer turns out to be one of the following -

  • Higher salary

  • AI is the future

  • AI is going to take all our jobs

then I think you should take some time off and think more about it, before deciding to become a Machine learning engineer.

But if your answer turns out to be something like the following -

  • In search of new opportunities and challenges

  • Learn a new skill

  • Passion towards AI

  • To blend AI into present work to make it more efficient

then I think you are in the right mindset and probably start thinking about how to get started with Machine learning.

Pathway to Become a Machine learning engineer

To know about Machine learning, I recommend you to check out my blog post where I have discussed what is Machine learning, Machine learning use-cases, and the future of Machine learning - Why we need to know about Machine Learning? (ML001)

So, after you finally decided to become a Machine learning engineer, we will go through some major points which will be helpful for you in deciding how you are going to approach it. We will cover the topics listed below and go deep into each one of the topics.

Contents -

  • What is Machine learning?

  • Why learn Machine learning?

  • Future of Machine learning

  • Machine learning vs Data Science

  • What is a Machine learning engineer?

  • Understanding the ecosystem

  • Life as a Machine learning engineer

  • Roles and Responsibilities of a Machine learning engineer

  • Pre-requisite for a ML engineer

  • Courses and resources to become a ML engineer

  • Salary of a Machine learning engineer

What is Machine learning?

Machine learning is a mathematical method that learns hidden patterns by analyzing the data and then make predictions on similar unseen data without human intervention. In simple Machine learning is a technique which makes machines think like humans. It can efficiently learn the patterns from past data and make predictions.

Machine learning is powerful due to the fact that it can be used in various industries- like retail industries, hospitality industries, agricultural industries, healthcare industries, IT industries, and many more.

You can learn more about Machine learning in my blog post - Why we need to know about Machine Learning? (ML001)

Why learn Machine learning?

Recently, we are seeing a boom in artificial intelligence, data science, and machine learning. But this not due to the fact that these techniques and methods didn't exist before, but this is because of the vast amount of data that we have nowadays and also because of the world-class computer systems we have in the market. The introduction of fast GPUs and fast CPUs with a high number of cores and threads have kickstarted Machine learning research in a big way. You can learn more about why learn Machine learning in my blog post - Why we need to know about Machine Learning? (ML001)

Future of Machine learning

The main advantage of Machine Learning is its limitless applications. Nowadays, every industry is impacted by Machine Learning and artificial intelligence. It has helped industries grow and become efficient. Take for example, how machine learning has changed the healthcare sector. Nowadays, doctors are able to analyze a lot more data and come to a better conclusion. The medical scanning analysis has taken new turns and some of the algorithms provide better accuracy than humans' interpretation. So, being a Machine Learning Engineer in the era of growing automation can be very fruitful.

With the world moving towards automation, there is a greater need for solving complex problems and that's where Machine Learning engineers come into the picture as they are the ones who can solve these complex problems using Machine learning techniques.

Machine learning vs Data Science

Data Science

Data Science is the process of data analysis to produce rich insights about data that is of some business value. Suppose a user has visited an e-commerce website, then he or she generates data, which is stored in the backend, and this data is later on analyzed by data scientists. Based on the insights from that data, the system understands the user's behavior and shopping patterns, based on which it retargets advertisements and suggestions so as to make the user buy a specific product(s).

A data scientist mainly deals with - data collection, data cleaning and aggregation, data analysis, data visualization, and at the end collects valuable insights from the data. A data scientist or a data wizard as I call it is responsible for making the weirdest connection between data and the business. Previously, we didn't have enough data to produce rich insights about businesses, but now with data, we have that leverage and that's when a data scientist comes into play. A data scientist is someone who brings up insights from chunks of data that otherwise would have gone unnoticed and understands the hidden patterns from that data.

From the business point of view, a data scientist looks after areas like predictive analysis, customer behavior analysis, operational shortcomings, supply chain cycles, competition overview, and more. We will cover these topics in detail in another article.

Machine learning

In simple terms, Machine learning is a part of data science. Machine learning mainly deals with statistics and algorithms which are applied to the data. When there are huge chunks of data, the data scientist often finds it very difficult to work with and that's when a machine learning engineer comes into the picture. The machine learning engineer uses machine learning algorithms like Linear Regression, Random Forest, Naive Bayes, and many more. These machine learning algorithms are then applied to the data, and then it learns from the data. Machine learning algorithms are highly sophisticated and need proper knowledge to understand the algorithms and its working.

What is a Machine learning engineer?

So, we have reached the point where we will be discussing our main point of this blog, which is what is Machine learning engineer. A machine learning engineer is someone who is skilled with probability and statistics, one who is good with differential calculus, good with algorithms, and last but not the least, should be good in any programming languages (preferably Python). One whose job is to work along with data scientists and make sure that whatever models they are using for the given data, works well and after which the data scientists can go forward and discuss the insights from data to the stakeholders of the company. So, a machine learning engineer's work is to understand the data first and find out the hidden patterns in the data through an array of models or build a custom model that works best with the given data.

They use programming frameworks and big data methods to make sure that the data pipelines are collecting the raw data and using them to make the machine learning models more efficient and reliable. They also work towards making sure that the machine learning applications that they build also work in realtime and provide the best results.

Understanding the ecosystem

For understanding the AI ecosystem, we have to understand the state of AI and its past. AI research came early into existence in the early 1960s after which it got faded away. But with recent advancements in the fields of computer systems and the big data boom, the AI ecosystem started to begin. Fast computer systems and high-speed processors coupled with a graphics card can process TBs of data within no time. Companies starting to gather data from all sources has also accelerated AI research in different fields.

Coming back to the AI ecosystem, currently, we are focused on shallow AI or which can be termed as narrow AI too, basically means that the AI systems are not generalized across spaces. They are capable of doing simple tasks and tasks which they are trained for. The AI which we see in fictional movies is called Artificial General Intelligence (AGI) which is a topic in itself. We will later cover AGI in future blog posts.

AI ecosystem consists of many segments which includes - Machine Learning, Deep learning, Artificial Narrow Intelligence. Machine Learning is a subset of AI which deals with algorithms whereas deep learning allows us to imitate a human mind which finds patterns in data and provides improvements in technologies like self-driving cars, facial recognition, etc. Artificial Narrow Intelligence deals with narrow AI tasks that are limited to specific tasks.

Life as a Machine learning engineer

I will post How I spend a day as a Machine learning engineer in a later blog but for now, I will discuss life as a Machine learning engineer. The machine learning engineer job in the IT sector is seeing heavy growth. As more and more research work is being done in AI, companies are now including AI in their workflow, be it Health, Pharma, IT, Finance, Manufacturing, Retail, Hospitality, and many more. So, naturally, companies are rapidly hiring Machine learning engineers to cater to their AI needs. In the next decade, we will see a lot of machine learning engineers being hired to help companies integrate new technologies into their existing workflow and become more efficient.

The daily life of a Machine learning engineer is similar to that of a computer engineer but they are more focused on creating algorithms and pipelines to make the machines self-learnable. Machine learning engineers may be deployed in various industries, he/she has to understand what kind of data is coming in, how to create efficient models to train the data, and how to deploy it at the edge to cater to the needs of that industry. (Note - Data from various industries will be completely different from each other, so a Machine learning engineer has to first understand the data and accordingly build machine learning models and later on finetune machine learning models to make them more efficient).

A machine learning engineer apart from doing his daily work which is already exciting as he/she gets to work with real-world data also needs to keep themselves updated with all the latest AI research taking place (including AI research papers, AI Journals, AI open-source projects) which will make sure that they stay ahead in the field of AI.

Roles and Responsibilities of a Machine learning engineer

A machine learning engineer as we all know deals with data. Now we will be discussing in detail how a Machine learning engineer really does as we dive deep into it. The machine learning engineer works with big data and tries to find out patterns in that data using machine learning models which helps him find out patterns in that data.

The machine learning engineers are responsible for making sure that the models work with big data as the volume of data which they work with will be huge. They need to scale their machine learning models in order to cater to the needs of ever-increasing real-time data. In the next few points, we will be discussing the responsibilities of a machine learning engineer and their roles.

The roles and responsibilities of a Machine learning engineer are -

  • Understanding the computer algorithms, data structures and computer architecture to be able to easily scale up

  • Applying the mathematical theories and formula to understand the algorithm or to make custom algorithms

  • Make data pipelines and applications for products which can be easily scaled

  • Using different techniques and models that best suit the data and can derive the hidden patterns in that data.

  • Demonstrate the algorithms and the models to the data scientist in an easily understandable way

  • To be able to discuss with the stakeholders and the business persons about the strategies and understanding their requirements

  • Help other computer engineers working alongside to better integrate the machine learning flow into the application

There are different types of machine learning engineers ranging from applied machine learning engineers to core machine learning engineers. An applied machine learning engineer is someone who focuses on computer basics and fundamentals while applying machine learning models at a higher level. While a core machine learning engineer is one who focuses mainly on machine learning algorithms, design custom machine learning models, and creating the data pipelines which ensures scalability. Depending on the requirements of the company, a machine learning engineer needs to adapt to the scenario.

Pre-requisite for a Machine Learning engineer

Machine learning engineer job is one of the highest paid jobs of the 21st century. The pre-requisite for being a Machine Learning engineer ranges from statistics to deep learning. One should have knowledge in statistics, calculus, linear algebra in mathematics. One should also have knowledge of machine learning, deep learning. Below we will list out the pre-requisite for a machine learning engineer -

  • Good with any programming language (preferably Python, and C++)

  • Good with calculus, statistics, linear algebra

  • Good with machine learning and deep learning

  • Additionally one can have knowledge in NLP, speech recognition and reinforcement learning

We will be discussing the prerequisite in more detail in our upcoming blog on - Pre-requisite for a Machine Learning engineer.

Courses and resources to become a ML engineer

There are many online resources out there that are very helpful to get started in AI and machine learning and if you ask me how did I get started on AI. Then I would say that these online courses really helped me get started on AI and machine learning. I have personally done several MOOCs and they have really helped me to understand the basics and first principles of all the important machine learning concepts out there which are very handy while working on real-world problems. I would like to name a few of them to get started but I will be covering each of them in detail in our upcoming blog - Best Online MOOCs to become a Machine learning engineer. So, let's discuss a few of them in this blog -

  • Machine learning course by Andrew Ng available in Coursera Platform - Link

  • course on Deep learning by Andrew Ng available on Coursera Platform - Link

  • IBM Data Science Professional Certificate Course by IBM available on Coursera Platform - Link

  • Machine Learning A-Z™: Hands-On Python & R In Data Science by Hadelin de Ponteves available on Udemy - Link

  • Deep Learning A-Z™: Hands-On Artificial Neural Networks by Hadelin de Ponteves available on Udemy - Link

These are a few courses that will help you to get started with Machine learning and AI and they also offer hands-on experience and working on small projects during the courses. These Machine learning MOOCs also comes with assignments and quizzes. If you follow them well then you will have a good hands-on experience with Machine learning.

Salary of a Machine learning engineer

As a fresher, a machine learning engineer is expected to earn anywhere between 100 thousand dollars to 140 thousand dollars per annum. Whereas in India, a machine learning engineer is expected to earn anywhere between 9 lakhs per annum to 15 lakhs per annum.

With more demands for Machine learning engineers, I see this trend going up every year which is great news for the AI community. It highly depends on the company and type of work you do, experience also comes into play. Companies like Google, IBM, Microsoft, Amazon, and other tech companies are expanding a lot in AI and you will see more and more jobs coming your way very soon or already it has started.


Hope you liked my article. If you have any questions and doubts related to this topic or any topic in AI and machine learning, do let me know in the comment section, and I will be more than happy to help you out. Do hit like on this article and share it among your friends who are in AI. Follow us on Instagram and Twitter - @theaibuddy. Let's democratize AI.

505 views0 comments
What is Machine learning?
bottom of page