We have come so far in technology that things that used to be fiction for us have become a new normal in our day-to-day life. You possibly wouldn’t have imagined how a robot could work as a waiter in a restaurant to serve a café, right? But today, we have passed all of those barricades. Thanks our innovations and the contributions, that they have given so far are impeccable.
The era of machine learning and artificial intelligence has already begun and believe it or not it has been quite an impressive journey so far and its application has also made a significant impact in our lives. This, implementation of “no-human” has become so popular that companies are now grinding the market in the search for exceptional machine learning engineers. It has come a long way from “Concept” to “Implementation”. So, let’s understand first in this article what actually a machine learning engineer is, and later we will see some interesting insights like career, salary graph, and future scope. So, let’s begin.
Who’s a Machine Learning Engineer?
To begin your career as a Machine Learning Engineer Python is the first step to start with. To begin your career, a basic understanding of Python is a must, and Python Programming Foundation – Self-Paced will definitely help you to become one. After having enough knowledge of Python, the next step is getting into Machine Learning. Again, Machine Learning Basic and Advanced – Self-Paced ensures that you get quality knowledge with all the required technical concepts.
Nevertheless, Machine Learning Engineers and Data Scientists aren’t very different from each other, they all fall under the same binary tree. Besides this, ML Engineers should have the exceptional capability to handle large sets of data to highlight the right and meaningful output. Machine learning engineers are responsible for designing, building, and fabricating different business models that are based on ML and are capable to cater the problems in any dedicated field/industry. The machine-learning engineers are also responsible for working closely with different business models that include artificial intelligence too and they should possess skillsets in every aspect of model architecture, data pipeline interaction, and metrics interpretation.
Concepts Machine Learning Engineer Should be Familiar With
If you’re going to pursue an ML Engineer job role, below are some of the highlights with which you must be familiar:
- The ML Engineer must possess the skill set of foundational concepts for building apps, infrastructure management, core data engineering, and its governance.
- A deep understanding of training, retraining, deploying, scheduling, monitoring, and improving models, besides this, the engineer must possess the skill set to design and creates scalable solutions for optimal performance within the business.
- The ML Engineer must be familiar with DSA, dynamic computability, and complexity along with the architecture
- Methods that include statistics, probability, data modeling, and evaluation must be well prepared to get a strong grip on basic implementations
- ML Algorithms and APIs
Career Insight For Machine Learning Engineer
Machine Learning can be a turning point in your life if you’re interested in algorithms, data science, automation, etc. Currently, there are hundreds and thousands of opportunities for machine learning entry positions and surprisingly countries like the USA are still facing the scarcity of not having enough ML Engineers. By this you can imagine the demand for ML today, when the demand is high, the scope becomes much more bright in such a field.
The urge for ML has drastically increased since the COVID pandemic and organizations of all sizes are welcoming this technology with open hands and the race to catch the pace is so trending. Companies are now introducing new methods and techniques to acquire more business with advanced technology and this has increased the rapid figure of ML Engineer jobs right from 23% to 31% (between 2012-2022), and the required learning skills jobs for ML are likely to get a huge bump of 75% in the next 5 years.
Below, is the chart for the ML Engineer job trends that you should definitely look into:
So, the more demand rises, the more competition will get increased and so do the job opportunities and this will proportionally make ML jobs one of the most trending occupations in the tech industry.
Salary Insight For Machine Learning Engineer
Being one of the most trending jobs in the industry today, the salary in-sights are quite extinguishing. That is why the demand for ML engineers is high but companies require individuals to have adequate knowledge and skillsets in order to perform different projects using different methods. As per a recent survey, the demand has drastically increased and today, millions of jobs are being posted just for ML engineers. This is one of the primary reasons machine learning salary is on the higher side and it is being considered one of the hottest jobs today.
The average salary for ML Engineers ranges between INR 9LPA – 11LPA in India and USD 1,33,000 – 1,42,000 in the USA and for the rest of the world (figures are based on annual compensation including bonus).
Let’s understand the salary insight for better clarity with this graph:
Besides this, salary is directly proportional to experience and knowledge. If you have a good experience with proper knowledge, you can expect a high salary.
Future Scope for Machine Learning Engineer
Being one of the most promising careers in technology today, the ratio of ML Engineers has exploded to almost 350% in the market today. Where job postal websites like LinkedIn were publishing 10k – 15k jobs 2-3 years back has now drastically changed and currently, this figure has risen to 35k job posts alone in India and 331k worldwide.
As you can see the demand and popularity are increasing every day, and the fight to ace in career is still on. ML Engineers are always considered the highest takeaway guys in the tech industry and their salary goes easily above 20 LPA (for experienced), and this is probably one of the best examples of the “Low Supply – High Demand” graph.
Speaking of its reach, the scope of Machine Learning is not limited to any particular sector, and it is expanding on a mass level all across the different sectors, such as Finance, Media, Gaming, etc.
Conclusion
These stats and figures are a clear symbol of its dominance in the tech industry for pacing towards revolutionizing the world for a better future. The demand for ML is not going anywhere in the upcoming future for the next 10 – 15 years and it’s not wrong to prejudice that this is the new future of a better world of automation and digitalization and from the perspective of a career it’s not less than the most glorious profession today.
