Machine learning engineering is one of the highest-paying jobs in the IT industry.
Tech-related jobs are getting more popular across the board. Positions in areas such as software development generally have a large number of job openings, but machine learning (ML) engineer roles are becoming more common and more desirable, thanks to their high salaries and faster growth.
Machine learning engineers are advanced programmers who develop artificial intelligence (AI) machines and systems, which can learn and apply knowledge by using complex data sets and algorithms. The use of artificial intelligence is becoming widespread, as applications increasingly rely on the technology to collect, process and utilize large amounts of data. As machine learning is a relatively new area with a massive future, following a career path in this field could be a rewarding and lucrative choice.
A recent report from the popular online job portal Indeed ranks “machine learning engineer” as the best job of 2019 due to growing demand and high salaries. The career offers a current average salary of $146,085, with a growth rate of about 344 percent in the past year.
Title: Machine learning is the highest-paying AI job
Source: Forbes.com
A popular career
While data scientist positions are more common than machine learning engineers, the demand for ML is growing. This is due to the increasing popularity and service in the machine learning industry. Most companies are using ML and AI as their major data initiatives. A growing number of companies are investing in machine learning, leading to a growing demand for more ML experts engaging in cutting-edge research.
Source: Forbes.com
There has been a significant increase in machine learning patents during the period between 2013 and 2017 – patents increased at a rate of 34% CAGR within the four years. Most of those patents stem from major tech industry players, including IBM, Microsoft, LinkedIn, Facebook, Intel, and many other renowned companies.
These numbers support the prediction that ML and AI jobs will be critical in the future, with companies looking for skills in Python, SQL programming, and other areas that are crucial to machine learning job roles. While both machine learning engineering (MLE) and Data Science (DS) roles need a solid software engineering background, the career path for ML jobs could include roles as a research assistant, software engineer, technical data scientist, or system engineer.
How to prepare for a career in machine learning?
To be a machine learning engineer, you must have experience applying machine learning methods to products, features, and services, such as pipeline work, minimizing footprints, or similar applications. Expertise in Python and/or C and C++ computer programming is key to this career field.
This industry also demands a strong background in at least one area of machine learning, such as robotics, gesture recognition, multimodal fusion, deep learning, or computer vision. You must have an excellent algorithm and data structure skills, such as complexity analysis and optimization.
The ideal candidate also needs the demonstrated ability to quickly learn and update large, existing code bases. Skills in implementing algorithms published in technical papers or the ability to develop new algorithms are also key. In addition, MLE hopefuls should be constantly striving to learn state-of-art and success-oriented ML techniques. Excellent collaboration and interpersonal skills are important attributes for becoming a machine learning engineer.
Here’s how to become a machine learning expert.
Step 1: Understand the basics
Increase your general knowledge of machine learning and data science. To be an expert in the field, you have to understand the deeper details of the industry.
Some suggested topics to explore are:
- Data science
- Big data
- Machine learning
- Artificial intelligence
- Analytics
Step 2: Learn statistics
Understanding and interpreting statistics is crucial for machine learning professionals. It is important to learn topics like data structures, sampling, random variable distribution, and linear and multiple regression.
Step 3: Learn Python
When it comes to pursuing ML courses, Python and R are the best programming languages.
The next step is to create a data analysis project and unsupervised and supervised learning models. Every ML expert deals with big data, regardless of their industry specialization.
You should also explore deep learning models that cover topics such as artificial neural networks, natural language processing, and convolutional neural networks.
Career options for Machine Learning
ML is projected to add $2.6 trillion in value for sales and marketing by 2020, and it has the potential to add about $2 trillion in the manufacturing and supply chain industry.
The average salary for machine learning engineers is around $142,858, making it the highest-paying AI job title of 2019.
In addition to software engineers, students pursuing ML education have a wide range of job opportunities as the world automates a significant number of processes that are currently done manually.
Software engineering is the most common starting point for professionals who want to make a career in machine learning, but it’s not the only path. Here are a few specializations that can lead to a solid start for an ML expert.
Source: Forbes.com
1) Natural language processing scientist
Natural Language Processing (NLP) Scientist involves enabling machines to understand human language so that machines can talk with humans in a natural way. An NLP Scientist helps to create machines that learn and mimic patterns of speech and translate it into other languages. An NLP Scientist should be an expert in the syntax, spelling, and grammar in their specific language in addition to machine learning.
2) Data scientist
A data scientist is one of the most appealing jobs of the 21st century, according to a Harvard Business review article. Data scientists use advanced analytics technologies such as predictive modeling and ML to collect and analyze massive amounts of data and generate useful insights, which are used for business decisions. So in addition to skills like data mining, statistical research ability, machine learning is the core skill for a data scientist.
3) Software developer
Software developers create flow charts that enable programmers to do their job. These professionals also develop the underlying infrastructure which allows computer networks to function. Sometimes, they also design specific computer functions.
Machine learning software developers are responsible for ensuring that upgrades work well. They provide documentation for the systems that assist the machine’s ongoing maintenance. Their core responsibilities include strategic planning, models and diagrams creation, and testing machinery. They should have a good knowledge of computer science, data structures, and other components of computer architecture, including memory and caches
4) Human-centered machine learning designer
These designers develop systems for process information and pattern recognition that eliminates the need to manually design programs. Their work leads to an improved and smarter user experience.
Human-centered machine learning designers create software for large companies, such as social media platforms looking for algorithms to customize a user’s feed or banks who need to support online transactions. For a bank, their machine learning systems need to handle and process vast numbers of financial transactions and identify fraudulent ones. Machine learning designers can help create systems to support these companies.
5) Computational linguist
Machine learning technologies often work with voice-recognition software. This helps users to navigate through telephone systems used within utility companies, banks, and government departments. Computational linguists enable these computer systems to learn how to understand the language spoken by human beings. These professionals are responsible for continually improve existing systems.
In addition to this, computational linguists are also responsible for assisting computers to learn various speech patterns. They help systems to be able to translate into other spoken languages. Most of the time, the goal is to enable the machines to comprehend natural languages. Computational linguists should understand the ways in which human beings use language for reproducing these capabilities in machines.
Future outlook of machine learning careers
According to a report from the World Economic Forum’s (WEF), AI-enabled machine learning jobs will reach 75 million roles by 2022.
The future scope of machine learning is huge. ML is applicable and useful in improving cognitive services, quantum computing, robotics, and almost all upcoming advanced technologies. In fact, ML is already a powerful tool that can do a surprisingly good job of solving problems such as classification.
In businesses, the demand for forecasting is becoming essential but also challenging with such large amounts of data, which often leads to erroneous results. At the same time, the trends in the demand data fluctuate frequently, and the inherent causes of those fluctuations are complex. It is not easy to understand demand variability for most business leaders and managers. In addition to these, manual factors intensify human bias in the activities related to demand planning.
For such businesses, machine learning is a widely-used forecasting tool. But it’s not just analyzing sales data – machine learning is finding a place in almost every area of daily life. With this increase in applications comes a rising number of job opportunities, and they tend to pay very well. Machine learning is an exciting field with a great deal of future potential, and talented software engineers and similar professionals should consider tailoring their pathway to a career in ML.
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