A Machine Learning Engineer works with programming and data to create intelligent machines i.e., AI. To do this, they must have sound knowledge in the field of data processing, machine learning, and AI. Let’s take a look at the role of a Machine Learning Engineer.
In today’s rapidly evolving digital age, mankind has been churning out enormous amounts of data. With the advent of smartphones, the Internet of Things and increasing networking, newer possibilities for data collection are emerging that were previously unimaginable.
Machine learning (ML) enables companies to use these huge amounts of data for applications that would not have been possible before. ML algorithms can learn the habits and buying behavior of customers, perform incredibly complex math, and enable completely new applications.
Almost every industry will be impacted by AI and machine learning including but not limited to medical, IT, gaming, communications, security, etc. Even now, every industry is fundamentally changed by the connection of data and logic.
In order to master this flood of information, human beings have now turned to automated processes and smart machines that can appropriately store and organize huge data sets.
Netflix, for example, uses machine learning on its recommendation service. Depending on what the user watches, Netflix saves data and the algorithm gives out recommendations based on this data.
This is where a Machine Learning Engineer comes in – an ML Engineer works on building AI and software systems capable of handling huge data sets and extracting meaningful information from them.
In need of an ML engineer?
Tasks and Responsibilities
The job of a machine learning engineer is to teach AIs and software to use data in an appropriate manner – in simple terms: A machine learning engineer is responsible for the development of ML systems that take over difficult tasks and replace slow human effort.
This is done by writing and developing programs or algorithms for these machines that will run and automate predictive models. ML Engineers are also responsible for data handling, result application, and the processing and optimization of AI systems.
Day to day tasks of a machine learning engineer:
- Write programs and develop algorithms to extract meaningful information from large amounts of data
- Preparation of data sets to feed into the algorithm
- Analysis and development of data structures
- Dealing with machine solutions for data processing
- Evaluation of the results
- Conduct experiments and test different approaches
- Optimize programs to improve performance, speed, and scalability
- Treat data engineering to ensure clean records
- Suggest useful machine learning applications
An indispensable skill for a machine learning engineer to have is a deep understanding of how data processing works. ML engineers are expected to have enough machine learning knowledge to understand the role of the algorithm in the application and to solve potential pitfalls and malfunctions.
To this end, he or she must have a sound education in computer science and, as a rule, advanced knowledge of mathematics. Knowledge of programming languages such as Python or C++ is also an essential skill.
R is also a programming language widely used for Machine learning that allows engineers to run tests, visualize data and use with machine learning algorithms.
Besides these hard skills, soft skills are also important. These include a high degree of accuracy and reliability, independent work and logical thinking, which they apply in a solution-oriented manner.
What must a machine learning engineer be able to do?
- Familiar with Machine Learning Algorithms
- Knowledge of ML programming languages – R, Python, Java or C++
- Experience with machine learning frameworks – Keras or TensorFlow
- Solid background in software development
- Ability to train machine learning models
- Know-how in data processing and handling
- Expertise in computer science and mathematics
- Accuracy and reliability
- Logical and solution-oriented thinking
To take up this profession, academic education is a requirement. This takes the form of a Bachelors’s degree in the fields of business informatics, computer science, maths, or related engineering sciences.
In addition, certificates are a great way to boost your resume and enhance specific skills required. Here are some courses and certifications to help you progress:
- Data Science: Machine Learning
- Fundamentals of Data Science: Computer-Aided Thinking with Python
- Specialization in Machine Learning
- Learn Python Programming Masterclass
Demand for Machine Learning Engineers
A commonly asked question when it comes to Machine Learning: What is the job market like? Is there a demand for Machine Learning Engineers? The answer is Yes! There is certainly a market for a promising Machine Learning career.
As businesses across all industries face exponential growth, the need to appropriately handle the resulting increase in data grows as well leading to a greater demand for professionals in the field. This means we can expect Machine Learning Engineer jobs to grow over the next years.
This includes freelancers as well! As discussed above, the potential applications of ML are endless. As a freelancer, you could work with specialized companies dealing with AI recognition, or help to manage risk and prevent fraud, or even work on data regarding customer insight and psychology.
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Machine Learning Engineer Salary
A Machine Learning Engineer can potentially expect to earn a salary on the higher end of the scale. The starting salary is usually in the range of $75,000 gross per year. The average salary settles at around $111,000 and a top salary is in the range of $153,000 or more.
How much does an ML Engineer earn?
The average freelancer hourly rate of a Machine Learning Engineer is $86 (freelancermap rate index – April 2020).