Our daily lives are increasingly getting mechanized – Google Home, Amazon Echo, Siri in iPhone, and Google Assistant in Android. Our reliance on machines to ease our work and make life convenient is increasing, thanks to machine learning engineers. Their work can be seen – from Amazon’s recommender system to voice activated refrigerators.
Artificial intelligence is permeating every sphere of human life.
Devices are becoming smarter, as they collect more knowledge about us, learn about us, and feel like us — machine learning techniques teach systems to do so. Now as artificial intelligence is gaining prominence, machine learning engineers are increasingly in demand.
Today, there 9.8 times more machine learning engineers than 5 years ago. In recent times, the number of jobs for machine learning engineers has spiked and several industry reports have projected this demand for machine learning engineers to increase. Thus, many experienced developers are taking AI certification and machine learning training to enter the realm of AI.
Companies aren’t behind. They are racing forward and making machine learning and artificial intelligence as their top priority. As per International, Data Corporation (IDC), as many as 61 percents of companies are already doing so. IDC predicts that spending on AI and ML will grow to $57.6B in 2021.
Who are machine learning engineers and what do they do?
Machine learning engineers are programmers well-versed in machine learning techniques, which teaches machines, to learn and apply without having to give specific instructions. The goal of machine learning engineers is to achieve artificial intelligence. How? They don’t only write programs for machines to perform a task, but also write programs for machines to perform specific tasks as required without any human intervention.
What skills do machine learning engineers have?
Machine learning engineers poses a sophisticated mix of skills. Starting from programming to distributed computing, machine learning engineers need to know a lot to be able to make a machine learn and take expected actions. Broadly, they need to know —
- Programming – R, Python, C++, and Java are required to work on various machine learning-related tools. For instance, Hadoop, required for data influx uses Java.
- Probability and statistics – Machine learning algorithms use probability and statistical models. For example, Gaussian Mixture models and Naïve Byes models are frequently used to understand various machine learning models.
- Data modeling and evaluation – A major part of building machine learning models is to continually check whether the model is accurate. For this, machine learning engineers require to know error models. Examples include log-loss for classification, sum-of-squared-errors for regression, etc.) for errors and evaluations, knowledge of training-testing split, sequential vs. randomized cross-validation, etc.).
- Advanced Signal Processing
Feature extraction is an important part of machine learning algorithms. It is required to find solutions to various problems. For this, advanced signal processing techniques can be used wavelets, shearlets, curvelets, contourlets, bandlets, etc.
How much do machine learning engineers earn?
According to Payscale, machine learning engineers earn an average of $73.9k in the U.S. This salary differs across the state. In San Francisco, the salary of a machine learning engineer is $169, 345. This is the median salary and varies with level of experience. Yet at entry-level, salaries seem to go beyond average salary of most tech jobs. Geographically, this is how salaries differ across prominent cities across the U.S.
- San Francisco: $169,345
- Boston: $156,181
- New York: $148,419
As mentioned earlier, many developers are driven towards machine learning because of its prospects. If anything, machine learning is an opportunity for them. Similarly, it presents abound opportunities for fresh tech graduates and all those looking to enter artificial intelligence space.