Machine Learning is the study of several patterns of previous data and algorithms that helps the computer to perform a specific task without any particular instruction. For example, if we implement artificial intelligence on a robot it will be able to work without previous experience. This is where we need the Machine Learning Engineer to implement ML on robots. They possess some machine learning skills to do jobs. In this article we will discuss in detail ML Engineer jobs, Salary, CV, Interview Question, Roadmap, Course, Certification, degree, and some Machine learning engineering book.
Who is a Machine Learning Engineer?
An ML engineer is someone who has some machine learning skills. With these skills, he/she perform some specific task. The tasks are done by the engineer are:
- ML engineer develops and implements several machine learning models, algorithms, techniques, and methods.
- With data scientists and engineers, they co-develop production-ready solutions.
- Guide the developer team tp perform any appropriate ML Task.
- Their responsibility is also to develop, implement, design and ship new algorithms.
- Provides feedback and various machine learning ideas to the development team.
Responsibilities of a Machine Learning Engineer
To become a machine learning engineer a person has to take various responsibilities and perform various duties to lead the team. Some of the remarkable responsibilities are:
- Transform data science prototypes after proper study.
- He has to design the mission learning systems.
- Implement the machine learning by various algorithms.
- The engineer develops mini machine learning applications.
- Another main responsibility is selecting the appropriate database.
- Train and retain the system’s unnecessary.
- Finally, recommend the courses of action to implement machine learning.
What Does an ML Engineer Do?
In real life, a machine learning engineer performs various activities as his day to day to day job duties. Some of the working functions of ML engineers are:
- Create and maintain a machine learning solution to solve a business problem.
- Optimize dose solutions for performance and scalability.
- Search business problems like reducing customer churn, campaign and improving product experience.
- Contributing to cutting-edge research in artificial intelligence and machine learning.
Roadmap to Become ML Engineer
There is a guideline to improve the career machine learning or learning path of becoming an ML engineer. The gateways are narrating below:
1 . Improve Your Math Skills
Mathematics is fundamental for any type of computing. It is very special to help you with how much in learning words and various algorithms. Three of the main concepts of mathematics which are required for machine learning are:
a. Probability and statistics
Now we are going to discuss the details of probability and statistics. It also has four subdivisions
Bayes Theorem: The Bayes Theorem the name-based algorithm to categorize your data.
Probability Distribution: This helps you to determine how frequently a particular event will take place. For example, you can determine the insurance premium based on probability distribution.
Sampling: sampling is the random collection of data from a particular set of data.
Hypothesis: the hypothesis is an example of data from the model of data
b. Linear Algebra
Linear algebra has two main concepts which are matrices and vectors. both of them are used widely in machine learning.
Matrices: it is used for image recognization the entire images you are using are in the form of matrices. It performs very simple applications like addition, subtraction, division, inverse transposition and so on.
Vectors: You will the recommendation system on YouTube, Amazon and Netflix are based on the calculation of vector. It helps you find out the customer behavior.
c. Calculus
Calculus is another common mathematical tool for learning engineer. We have differential calculus and integral calculus. This helps you to provide the probability of events.
2. Programming Skills
The next step of the machine learning career path is to develop the skill of programming language. There are lots of programming languages like C, C++, R, ruby, swift, python, Java, etc. If we see the market dominance of the last 5 years will see the success of python and R. The programming language Java is also successful to develop a machine learning project. FossGuru recommended you to learn python and R as the key tool of machine learning.
Python: Python is object-oriented programming that reliant on its many packages. It is suitable for integrating with other software.
R: R is a functional software. Because of its built-in package feature, it is faster than python. It is more suitable for statistical analysis.
3. Data Engineering Skills
Data engineering skills help you to analyze and process your data as soon as you get it. There are three steps in data engineering.
a. Data Pre-processing
Before data processing, it is required to perform pre-processing activities. Data preprocessing steps include cleaning, parsing, correcting and consolidating.
b. Extract Transform Load
You need to know how data can be extracted from the local server or the internet. You need to know how to transform data. For example, all types of data are not accepted by the program. So you need to convert the data as your required form to run the data on the program. Finally have to know the loading procedure of data in the program.
c. Knowledge of Database
The last step of data engineering skills is knowledge about databases. the knowledge about DBMS or database management software is very essential.
4. Machine Learning Techniques
To become an ML engineer have to know the techniques of machine learning. Supervised machine learning and unsupervised machine learning are the most common techniques of learning. Supervised learning is used for working with a predefined data set. Finding spam of email, risk assessment fraud detection, and image classification is the example of supervised learning. Unsupervised learning does not require for any predefined data set. Market basket analysis semantic clustering and identify accident-prone areas are the example of unsupervised learning.
5. Learn Machine Learning Frameworks
Machine learning frameworks help to abstract the complex parts of machine learning and make it available to a large group of developers. Most of the common machine learning frameworks are TensorFlow, Keras, Scikit learns, Theano, Spark MLlib, Caffe, Torch, and Pytorch. After learning the framework you are completed the basic requirement to become a machine learning engineer. Now you have to apply for a job and get the highest salary.
Salary Trends of a Machine Learning Engineer
There is a question for every job about the salary. The salary of a machine learning job is much higher than the Cloud computing job. According to Glassdoor, we are showing the Everest base pe of machine learning engineer salary.
In this graph, we can see the average salary of a machine learning engineer in the United States is $121,707/year. You will be astonished if you see that even a lower salary is $83k.
If you see the second figure then you can simulate the salary of machine learning engineer in India. The average salary of them is 750k rupees. The living cost of India is very low and getting a machine learning job is much easier than the UK.
The Job of ML Engineer
The machine learning engineer gets the job much faster than other engineers. There are lots of job portals who are offering these types of jobs. Indeed and JobStreet more famous for them. If we browse indeed will get a thousand job posting available for the engineers of machine learning. The recruiters focus on expertise in artificial intelligence.
Top Companies Hiring to ML Engineer
Almost all the big machine learning service provider already hiring ML engineers. Besides the service providers Facebook, Microsoft, Google, Cisco, Uber, Apple, Amazon, and Deloitt process hiring for machine learning engineers. the small components of machine learning are also having engineers who have expertise in deep learning, decision tree, neural network, and Quantum computing.
Skills of a Machine Learning Engineer
To get a good job in the field of machine learning the engineers should have various skills to run the team or become part of the machine learning implementation team. do skills will identify him and differentiate from the other engineers.some of the must-have skills of ML engineers are:
1. Programming
Programming knowledge includes the ability to adapt, implement and make changes to the programs when required. Programming languages like C/ C++, Java python studio and JavaScript are common. in my previous article, I have written about the best 20 machine learning language for the engineers. If you feel you can follow the article to know the most common machine learning languages.
2. Applied Mathematics
Applied mathematics includes understanding the matrix, derivatives, and integrals. It is necessary to understand the concept of statistics like mean, median, Mode, standard deviation, correlation, regression, hypothesis and time series analysis. Standard deviation is very important to implement machine learning, data science, and deep learning. Understanding of probability algorithm like Naive Bayes, mixture models is also required.
3. Data Modeling and Evaluation
Data modeling is the process of finding the hidden structure of a given data set and finding patterns such as correlation and plasters. A key part of the process is evaluating how good a given model is and choosing an appropriate accuracy or error measure and evaluation strategies. It is the very basis of the machine learning process. With proper implementation of the machine learning model, you can and make any ML project.
4. Machine Learning Algorithms and Libraries
As a machine learning engineer, you need a strong understanding of various libraries and algorithm concepts like linear regression, k-means, support vector machines, etc. You have to also know the working procedure of each algorithm. Having the capability to differentiate each item will give you and another dividend to become ML engineer. Finally understanding various packages, libraries and APIs like NumPy, Pandas, TensorFlow, and Scikit learn.
5. Advanced Signal Processing
To become a successful machine learning developer you must have the skill of advanced signal processing. If you can process the signal algorithms like Wavelets, Shearlets, Curvlets, and Bandlets then you will be the boss of machine learning.
6. Neural Network Architectures
A neural network is a specific set of algorithms that is specialized for machine learning. The help of a neural network the ML platforms like a human. It is inspired by biological networks and so-called deep learning networks. For the complex inputs to get the expected output we use the concept of neural networks in machine learning. Translation, image recognition, and specific recognition is the example of a neural network.
7. Language Processing
There are a bunch of libraries in machine learning. To summarise the data you need natural language processing. Audio and video analysis is an example of NLP. It can be also utilized for the non-audio data like the text to instruct the information by the signals.
8. Industry Knowledge
The knowledge of the non-tactical ML skills is also important to a machine learning engineer. Amazon machine learning developer you must know the industry and how it can be deposited in the real-life scenario. Without the proper utilization in real life, all of your in endeavors go to vain.
9. Effective Communication
If you have machine learning knowledge you have to express that in front of the audience. If you cannot sell your quality is not getting a good job and a good salary. As a team leader, you should also have effective communication skills to guide the other machine learning engineers. Without proper communication skills, nobody will evaluate that you are a learned person.
10. Rapid Prototyping
The last skill of today’s discussion is rapid prototyping. As a machine learning expert, you have to be smarter than others. You understand the trend of technology and capabilities to prototype rapidly as per the requirement of the vendor. In the real world, nobody likes the doubt and bullish works. So as an ML engineer you must be able to prototype all the real-life problems to solve efficiently.
Resume of Machine Learning Engineer
It is very important to understand the resume of a machine learning engineer. Today will discuss some key points of machine learning resumes for an engineer. After providing your full name, email ID and contact number you must narrate the summary of your career. It is very important to put a LinkedIn profile ID and GitHub profile link at your resume. On the next page, you can include your machine learning job experience and educational qualification. It will give you an additional advantage if you add some machine learning skills and the certifications of machine learning. For better assimilation, a machine learning job-oriented resume is given below.
Some Common Questions Asked By ML Engineer
In this section, we will answer some questions which are frequently asked by any ML student. It will help them to become a successful engineer. Some of the questions are:
What Should a Machine Learning Engineer Know?
As a machine learning engineer, you have to know many things besides the language. Understand the real-life scenario and industrial problem. If you do not know the business application of machine learning you cannot sell your quality. The knowledge of mathematical application, statistical problems, correlation, regression, time series analysis classification and association is mast. it will give an additional advantage if you know no decision tree, neural network, and prototyping. If you know the basics you can develop any project with the help of online machine learning courses and ebooks.
How Much Does a Machine Learning Engineer Earn?
This is the most difficult question who is a machine learning engineer frequently asked. There is no clear cut answer to this question. Everything depends on your quality. If you are a good machine learning developer you will be able to earn about $122 US dollar per year. ML salary depends from countries to countries. For example, machine learning salaries in Japan and India are not the same.
Is Machine Learning Engineer a Good Career?
Yes, ML engineer is a good career for the software developer. The salary range of the machine learning industry is higher than any other technological appointment. The job posting of Indeed, Jobstreet, etc. is remarkable. It is a good career for all levels of people.
Is it Hard to Become a Machine Learning Engineer?
To become a machine learning engineer should have an academic degree in computer science and engineering. without that admin degree, it is also possible to become an ML engineer. You have to know several machine learning languages, prototyping, data modeling and simulation, deep learning and neural networks. If you are newer in the field then and it will be hard for you.
Can I Learn Machine Learning Without Coding?
It is almost impossible to learn machine learning without the knowledge of programming or code. Without coding, you can be the team leader if you know the machine learning industry. But, nobody would care for you whoever you are.
Do You Need a Ph.D. to be a Machine Learning Engineer?
You do not need any Ph.D. degree to become a machine learning expert. Only the knowledge of programming, mathematics and real-life implementation is enough to become the engineer of machine learning. If you want to work for a strategical level then you may seek your Ph.D. degree.
How Fast Can I Learn Machine Learning?
There is no shortcut to success. If you want to be my real machine learning programmer you should go slow. To learn machine learning faster you can follow the video tutorials of YouTube. If you mail me I will recommend some best websites for machine learning.
Final Words
Now the machine learning line is very common in our real-life scenario. You do not require any formal degree to develop ML project. But several machine learning courses can give you some additional advantage to integrate your knowledge with your project. The skills I have mentioned is not mandatory but it will give you more dividend. Moreover, there is no alternative to practice to become a successful machine learning engineer.
