Machine Learning (ML) is a branch of Artificial Intelligence (AI) that uses data analytics and mathematical modelling to imitate human-like intelligence in computers. ML is the study of computer algorithms that learn through finding patterns in data which can be helpful for predictions in the future using data modelling. The more the data better are the predictions similar to humans, the more the practice better will be the accuracy.
As per the above graph, we can see that the trend for machine learning started in early 2015, and skyrocketed in 2018.
The article would cover the basics of Machine Learning engineering like work, expectations, needs, job profile, responsibilities, skills, compensation, etc.
- Who is a Machine Learning Engineer? Moreover, What do they do?
- Machine Learning Engineer Salary in India
- Machine Learning Engineer Salary Deciding Factors
- Why is Machine Learning Important In Today’s World?
- 1. Predict values based on data:
- 2. Identify unusual occurrences using historical data:
- 3. Find structure in data:
- 4. Predict categories:
- Machine Learning Engineer Job Roles and Responsibilities
- What are the Skills Required for a Machine Learning Engineer?
- How to Become a Machine Learning Engineer?
- 1. Learn Programming Language:
- 2. Learning Maths for Machine Learning:
- 3. Learning Basics of Machine Learning:
- 4. Framework and Packages:
- 5. Natural Language Processing (NLP) and Deep Learning (DL):
- Machine Learning Engineer Salary in Other Countries
- Summary
- FAQs
- 1. What is the Salary of a Machine Learning Engineer in India?
- 2. Does Machine Learning Pay Well?
- 3. Is Machine Learning a Good Career?
- 4. Who Earns More: Data Scientist or Machine Learning Engineer?
- 5. Is it Hard to Become a Machine Learning Engineer?
- Additional Resources
Who is a Machine Learning Engineer? Moreover, What do they do?
ML engineers decipher the raw data accumulated from different data pipelines into data science models that can be applied and scaled based on the situation. An ML engineer associates that organized data to the models characterized by the data scientists they work with. Moreover, ML engineers foster algorithms and construct programs that empower machines, PCs, and robots to process data and detect different patterns.
The work of a Machine Learning Engineer is very like that of a Data Scientist as the two jobs include working with immense volumes of data. Consequently, both Machine Learning Engineers and Data Scientists should have superb command over data handling. Nonetheless, that is every one of the likenesses that these two jobs share.
Generally, Machine Learning Engineers work in a joint effort with Data Scientists. While Data Scientists separate significant insights from massive datasets and impart the data to business partners, Machine Learning Engineers guarantee that the models utilized by Data Scientists can ingest extraordinary measures of ongoing information for producing more precise outcomes.
Machine Learning Engineer Salary in India
On average, a machine learning engineer earns between 7.5 to 8 lakh per annum as the total compensation. According to Glassdoor, the nationwide average is 7.6 lakh, while the Payscale data say it to be 7 lahks. Both these data are being calculated from approx 550 different profiles.
Machine Learning Engineer Salary Deciding Factors
The below four factors majorly impact the salary for this role:
1. Experience:
Total years of experience in the relevant domain help you understand the problems and give an appropriate production-ready solution. This is one of the deciding factors in your total compensation.
2. Location:
In the current remote working condition, location does not play a significant role in compensation but can have minor variations based on the cost of living and industry hubs.
| Location | Average Total Compensation |
| Bangalore | 8.7 lakh |
| Chennai | 7.25 lakh |
| Delhi | 7 lakh |
| Gurgaon | 5.35 lakh |
| Hyderabad | 6.8 lakh |
| Kolkata | 6.4 lakh |
| Mumbai | 6.25 lakh |
| Noida | 6 lakh |
| Pune | 6.15 lakh |
3. Company:
Company is one of the deciding factors in the total compensation. It is equally important as experience. It directly impacts your salary and benefits.
| Company | Average Total Compensation |
| TCS | 5 lakh |
| Accenture | 7.75 lakh |
| Cognizant | 5.5 lakh |
| Infosys | 6.3 lakh |
| 12.15 lakh | |
| Wipro | 5.6 lakh |
| Qualcomm | 14.2 lakh |
| Oracle | 10.35 lakh |
4. Skillset:
Skillset acts as the main door to the treasure inside. If you have appropriate skill sets, it will help you clear the interviews and perform well in the company, which will compound your compensation over the long term.
| Skills | Average Total Compensation |
| Machine Learning | 7 lakh |
| Deep Learning | 7.5 lakh |
| Natural Language Processing | 7.3 lakh |
| Computer Vision | 7.25 lakh |
| Artificial Intelligence | 8 lakh |
Why is Machine Learning Important In Today’s World?
Machine Learning and Artificial Intelligence, in general, have become an integral part of modern-day companies. Its usage and applications are not just restricted to the tech industry and can improve a company’s general working, efficiency, and dynamic interaction by digging into vast volumes of data. Below are some wonders that machine learning can do.
1. Predict values based on data:
Supportive in recognizing facts and logical results between variables, regression algorithms model from values used to make an expectation. Regression contemplates assisting with gauging the future, assisting with expected demands of products, anticipating marketing and sales projections, or gauging survey results.
2. Identify unusual occurrences using historical data:
ML can be regularly used to spot a possible threat, and irregularity discovery algorithms pinpoint information outside expected standards or ranges. Hardware glitches, text mistakes, unusual number changes in sales and fraud detection, and detecting illegal transactions, especially in the payments industry, are instances of how ML can be utilized to address these concerns.
3. Find structure in data:
Grouping or Clustering algorithms are usually the initial phases in ML, and it helps in uncovering the hidden subsets or substructures inside the dataset. Classifying everyday things into clusters is regularly utilized in market and competitor analysis, offering knowledge that can assist with choosing competitive prices and extracting client inclinations towards the product. It also enables recommending a product in the same cluster or group the customer has shown interest in previously.
4. Predict categories:
The categorization algorithm assists with deciding the correct classification for data. It is highly used in the e-commerce industry, where thousands of new products are listed every day, and they must be categorized appropriately to generate better recommendations. Manually categorizing thousands of products on a daily basis is a cumbersome and error-prone job.
Machine Learning Engineer Job Roles and Responsibilities
- Identifying, examining, clustering, and mining data and converting them to data models
- Mathematical conditions validation and data model performance analysis and optimization using previous test results
- Developing neural network models that support the business/customer use cases
- Design and implementation of deep learning-based models for production usage or contribution to opensource ML frameworks and libraries
- Deep understanding of data mining and mathematical algorithms
- Applying machine learning techniques to real-world problems
What are the Skills Required for a Machine Learning Engineer?
The fundamental thing required for any job in the tech industry is a grasp of any modern-day programming language. Talking just about machine learning roles, one should be proficient with one or more programming languages from Python, R, Scala, C++, Java, Matlab, etc.
On top of any programming language, one should understand frameworks like Keras, Tensorflow, PyTorch, etc. Talking about language-specific libraries and packages, one should know data analysis packages like scipy, NumPy, pandas, matplotlib in Python.
As an enormous amount of data is involved, substantial expertise is required to extract and process data. Experience in RDBMS and NoSQL databases is a must, and significant data processing ecosystems like Hadoop, Spark, or Hive are considered a plus point.
In addition to the above requirements, below are some of the skills which can help you thrive:
- Knowledge in any one or more cloud-based container ecosystems like Docker, Mesos, and Kubernetes.
- Basic understanding of Natural Language Processing and using deep neural networks like RNN, LSTM, GRU, CNN, etc.
- Knowledge in working with GPU, Cuda/CuDNN, profiling, and low-level optimizations.
The below image can summarize the basic techniques and algorithms used in day-to-day life, and theoretical and working knowledge and understanding in the same is a must.
How to Become a Machine Learning Engineer?
Becoming a sound machine learning engineer is a step-by-step process. I would describe the steps below, which will apply from the amateur level to the expert level. On top of the below-mentioned steps, relevant practice at every step is a must.
1. Learn Programming Language:
Python is one of the most used programming languages for machine learning tasks due to ease of learning, support from the community, and availability of resources, i.e., packages and frameworks. Start from learning the basics of programming language to advance concepts.
Once the concepts of Python are clear, one can move forward with learning Python-specific to machine learning, i.e. packages like Pandas, NumPy, SciLib, Matplotlib, Seaborn, Plotly, etc.
2. Learning Maths for Machine Learning:
Mathematics is the foundation stone for machine learning algorithms. Learning some of the basics to advance concepts is essential. The next step towards the goal is to gain expertise in statistics, Probability, Linear Algebra, Derivatives, and Partial Derivatives.
3. Learning Basics of Machine Learning:
Once the foundation stone has been fixed, gaining knowledge in the field becomes a must. Below is the list of some must-learn topics:
- Linear Regression
- Cross-Validation and Bias-Variance Trade-off
- Logistic Regression
- KNN (K Nearest Neighbours)
- Decision Tree and Random Forest Algorithm
- SVM (Support Vector Machine)
- K Means Clustering
- PCA (Principal Component Analysis)
- Recommendation Engine and Systems
4. Framework and Packages:
Many open-source frameworks are available that help implement some of the above concepts and algorithms with ease. Furthermore, learning these frameworks and using their libraries in our preferred programming language becomes essential. Majorly popular frameworks with excellent community support are TensorFlow, Keras, Torch, PyTorch, etc.
5. Natural Language Processing (NLP) and Deep Learning (DL):
Above all, the steps would help you become a sound machine-learning engineer with a great foundation. Now it is time to expand the horizon to gain expertise in some of the complex topics. At this point, one can start exploring topics like NLP, DL, RL (Reinforcement Learning), etc. Gaining expertise in any of the above areas would make an individual domain expert.
Machine Learning Engineer Salary in Other Countries
The salary or total compensation of a Machine Learning Engineer in countries other than India varies based on four factors, as mentioned earlier.
The median salary of a machine learning engineer in the US is USD 120k.
The average salary of a machine learning engineer in the UK is GBP 50k.
Preparing for Interviews is not just a time-bound effort that can be done within weeks or months. It is a continuous learning and improvising way of developing skills. Last-minute interview preparation can be done by revising the basic concepts on online platforms like InterviewBit. InterviewBit has a curated list of interview preparation questions that can be found here.
Summary
Machine learning engineering is a great career choice with many sectors like Banking, Finance, Transportation, Retail, Healthcare, etc., adopting the same. The pay scale is very competitive, and with experience, it grows exponentially.
FAQs
1. What is the Salary of a Machine Learning Engineer in India?
The salary of a Machine Learning Engineer in India is dependent on many factors including total years of experience in relevant fields, the scale of the company, the skills of the engineer, and the location. It starts from 6.5-7 lakh per annum as a total compensation in IT hubs like Bangalore, Hyderabad, Chennai, Pune, etc, and can go up exponentially.
2. Does Machine Learning Pay Well?
Yes, in the long run even if you have started from the lowest level depending on your capabilities and efforts put in to learn new things i.e. acceptance to change, machine learning can pay you hugely.
3. Is Machine Learning a Good Career?
In today’s fast-growing market, automation is one of the things people are looking for and for most of such automation machine learning acts as a base to solve the problem. In other words, its importance in today’s world is like salt in food which really makes it one of the best career choices.
4. Who Earns More: Data Scientist or Machine Learning Engineer?
Although, there is a thin line between Data Scientists and Machine Learning engineers if we go by the job description and responsibilities. It is very hard to select one option out of two based on the pay scale as most of the time they are very competitive and go hand-in-hand.
5. Is it Hard to Become a Machine Learning Engineer?
Developing new skills takes its own time and effort to learn the same. Similarly to that, becoming a Machine Learning Engineer requires proper learning and dedication to practicing what you learn. If you remain consistent and have the patience to digest the same throughout the process it’s never hard to learn.
Additional Resources
- Best Machine Learning Courses
- Free Deep Learning Course
- Machine Learning Interview Questions
- Machine Learning MCQ
- Machine Learning Projects
- Types of Machine Learning
- Machine Learning Books
- Machine Learning Applications
- Best Data Science Courses
- Deep Learning Vs Machine Learning
- Data Science Vs Machine Learning
- Artificial Intelligence Vs Machine Learning
