According to the O’Reilly study The State of Machine Learning Adoption in the Enterprise, 50% of their respondents claim to have adopted machine learning to a different extent.
The demand for machine learning engineers is growing with the development of technology and new discoveries in the world of data science. More and more organizations decide to work with machine learning specialists to enhance products and services. Working with technology helps to outgrow the competition and deliver an innovative solution.
Also, the same study by O’Reilly states that job titles specific to machine learning are already widely used at organizations with extensive experience in machine learning: data scientist (81%), machine learning engineer (39%), and deep learning engineer (20%). In the next few years, the growth will substantially continue and the statistics will show higher numbers.
Due to the specificity of the specialization, machine learning engineers are among one of the best-paid groups in the workforce. And it’s not without a reason. It requires deep, specialist knowledge, proper skillset, and adeptness in the world of science.
So how much does a machine learning engineer learn?
To find out how much let’s analyze it step by step, beginning from the basic concepts.
Machine Learning Engineer – what’s the role and why is it important?
The role of a machine learning engineer is so important and highly valued for a simple reason – not every person is predisposed to be one and the skills are worth its weight in gold.
So who is a machine learning engineer and what does he (or she) do? You might have heard about two names used interchangeably – machine learning engineer and data scientist. While the boundary blurs there are smallish differences.
First of all, data science is a broader term. It includes programming but focuses mostly on the data in analytical approach. So if you’re a data scientist, you analyze data and draw conclusions for your business that help improve it.
A machine learning engineer or specialist can be part of a data science team. ML is focused on building models that can later be used to, for example, improve a product.
Machine learning engineers can create an image recognition system that recognizes types of trash based on the photographs, predict the need for energy, forecast sale of a product in the future based on the historical data, or predict the course of the epidemic.
Some of the most known examples you might have heard of are traffic prediction and inventing a new drug that can kill many species of antibiotic-resistant bacteria.
And to do so, the ML engineer must have an in-depth knowledge of programming, probability and statistics, and is a master of mathematics and computers. These are difficult skills that not everyone is able to acquire and harness
These are just a few examples of the wonderful things an engineer can do with machine learning and data.
It’s a growth-focused role that many employers seek as it helps to develop business.
Machine Learning Engineer salary and the three levels of experience
The most important question is how much do machine learning engineers make? There is no specific answer. The machine learning engineer’s salary depends on a couple of factors, for example, the company, job title, country or even city. Yet the most important reason is experience.
Your experience and knowledge of the data science world defines the numbers on your account. That’s because the better knowledge you have, the more value you can bring into the company. And the more value you bring, the more you are paid.
Andrew Zola in his article defines three levels of experience of a machine learning engineer. Based on the data collected by the author, the salary looks as follows (for more detailed info, read the full article):
- Entry level machine learning engineer – (0-4 years of experience) – the average is approximately $97,090. However, with potential bonuses and profit-sharing, that number can rapidly rise to $130,000 or even more.
- Mid-level machine learning engineer – (5-9 years of experience) – the average salary of $112,095. With potential bonuses and profit-sharing, it can be $160,000 or more.
- Senior machine learning engineer – (10+ years of experience) – average salary of $132,500; with bonuses and profits the number goes up to as much as $181,000 annually.
In all three cases the skillset is familiar:
The skills may differ upon the field the company or an engineer is focused on. But the most important thing is what you can do with it and whether you’re willing to learn and adapt to changes.
It’s not surprising that some of the companies that make the most are those most known in the world. Respectively, in the top 10 you can find such brands as Apple, Intel Corporation, Facebook, LinkedIn, Spotify, IBM or Google.
Additionally, some of the best cities for machine learning engineers are San Francisco (California), San Jose (California), Seattle (Washington) and Boston (Massachusetts). That is because some of the best tech companies are located in these cities.
How to earn more as a Machine Learning Engineer?
How to get to the top? How to earn more as a machine learning engineer? Quality, authority and experience are the three indicators of a successful engineer. So make sure to posses these three. And to do that, you need to keep learning, developing your skills and be open for changes. There are many ways in which you can work on your skills:
Participate in Conferences, Seminars, and Courses – you can learn a lot about the industry, latest trends, and secrets from conferences. It’s also a great chance to meet new people, experts or even find a job.
Learn new skills – the best thing you can do is to harness the power of knowledge. There are many resources for machine learning that you can learn new things from and expand your current skillset.
Move to a different city – if you have to, you can move to one of the cities that will give you the opportunity to find a better job, maybe in one of the best companies
Join communities – there are numerous forums and communities on the internet where people learn, teach, and meet other people, exchange ideas and encourage each other to work harder. Maybe there’s a local community where you live? Join such a place and look for inspiration. Try Kaggle, Stack Overflow, or Reddit.
Networking – try to talk to as many people as you can when you’re participating in conferences or if you are part of a community. Having a network of trusted people can help you grow your career. Reach out to people who inspire you to expand your social network.
Read books – there are some great books out there that can help you become a better machine learning engineer and even become one from a scratch.
Try whatever you like, mix, and don’t give up.
In the coming years, perhaps we may expect a boom for the machine learning engineers as companies will work hard to develop and create modern products. And one of the best ways to grow as a company is to invest in technology and AI that helps to go forward.
Thus, if you’re a data scientist, there is a bright future ahead of you.
References (A list of full resources used in this article):
1. The State of Machine Learning Adoption in the Enterprise by O’Reilly
2. How to combine some machine learning methods for traffic prediction? by Mahdi R. on Hacker Noon, June 14th 2018
3. Artificial intelligence yields new antibiotic by Anne Trafton | MIT News Office, February 20, 2020
4. Machine Learning Engineer Salary Guide by Andrew Zola on Springboard Blog, July 19, 2019
5. Machine Learning Engineer Salaries by Glassdor
6. Average Machine Learning Engineer Salary by PayScale
7. How to Become a Machine Learning Engineer by Robert Half on roberthalf.com8. Data Science vs. Data Analytics vs. Machine Learning: Expert Talk by Srihari Sasikumar on SimpliLearn
MLOps: What It Is, Why it Matters, and How To Implement It (from a Data Scientist Perspective)
13 mins read | Prince Canuma | Posted January 14, 2021
According to techjury, we have produced 10x more data in 2020 compared to 2019. For data scientists like you and me, that is like early Christmas because there are so many theories/ideas to explore, experiment with, and many discoveries to be made and models to be developed.
But if we want to be serious and actually have those models touch real-life business problems and real people, we have to deal with the essentials like:
- acquiring & cleaning large amounts of data;
- setting up tracking and versioning for experiments and model training runs;
- setting up the deployment and monitoring pipelines for the models that do get to production.
And we need to find a way to scale our ML operations to the needs of the business and/or users of our ML models.
There were similar issues in the past when we needed to scale conventional software systems so that more people can use them. DevOps’ solution was a set of practices for developing, testing, deploying, and operating large-scale software systems. With DevOps, development cycles became shorter, deployment velocity increased, and system releases became auditable and dependable.
That brings us to MLOps. It was born at the intersection of DevOps, Data Engineering, and Machine Learning, and it’s a similar concept to DevOps, but the execution is different. ML systems are experimental in nature and have more components that are significantly more complex to build and operate.
Let’s dig in!Continue reading ->