AI is a trending field because of the enthusiasm towards human-computer interaction. Today, technology is focused on convenience; machines are designed in a way that they can communicate with human beings.
While it is within the programming umbrella, it is an advanced stage. Developers in the field are referred to as machine learning engineers. It is about merging human language and machine language. With AI, everything is possible; most tasks in production are being replaced with machines. Although they make decisions as per the embedded code on familiar tasks, they can change reaction and response based on context. This is the ultimate of AI.
Originally, AI was linked to unemployment because it was obvious that a machine in the production will replace people. The claims were true because real case scenarios can be cited to prove the job replacement. However, this does not apply in the technology world. It is the opposite, and the fears are unwarranted.
AI is a channel for employment. Machine learning engineering is only one of the many professions that come with this technology trend. While the machines are independent, they had to develop and maintained on a regular basis, which creates the need for skills. As more firms consider integrating the system to their business processes, the more jobs are created for related skills. It is a high qualification in the technology industry.
Are you interested in being a machine-learning engineer? There are endless opportunities in this career. You only need to know where to start. Here is a guide towards establishing a career in machine learning.
What is it?
Machine learning is about empowering computers; the ability to learn and act accordingly in different environments. In this field of technology, systems can enhance their skills after completing a task severally. Just like human beings, expertise comes with experience.
Unlike in normal programming, machine learning entails predictions. Computer systems are integrated with sets of data, which is the basis of their action. Machines apply knowledge from existing data, train, and perform according to the existing scenario. From the data sets, a machine can recognize speech, objects, as well as faces.
Why the increase in demand?
Since 2015, the number of jobs in AI has exploded in all industries. Search engine statistics also demonstrate a sharp increase in the keyword search. Employers in every field are after the talent in IT. It is referred to as talent because of the creativity involved. There are no definite structures and functions in developing these machines. They are designed for a wide variety of functions in different fields.
The demand for machine-learning engineers has not yet been met. No company wants to be left behind with the trend in the business world. It is not only a way of maintaining market position but also satisfying customers because consumers expect the efficiency and convenience that comes with AI.
Machine learning has magical effects on a company; it can transform a firm into an industry leader.
What do machine-learning engineers do?
There are multiple roles for this job in the current business world. Here are some of the top paths of this career
- System design and software engineering
- Application of machine learning data sets
- Data modeling and evaluation
- Relevant programming languages
Experts list Python and R as the key programming languages in machine learning. The two are also popular in data analysis and evaluation. Java is also cited among the top languages necessary in machine learning.
Although mastering a language is crucial in this career path, efficient and effective systems go beyond choosing a programming language. It is more on design, controlling flow, resilience, and fault tolerance.
What skills do you need?
Computer science and programming is a requirement. Also, you need to master statistics, problem-solving and data science.
The major skills required to be an expert in machine learning are;
- Basics of computer science and programming
- Probability and statistics
- Data design and evaluation
- Application of machine learning algorithms
- Software design and engineering
Each of the five has multiple sub-skills.
Of course, we have to talk about pay. Among the AI related jobs, Machine-learning engineering comes third in salary after a director of analysts and principal scientist. On average, a machine-learning engineer in the US earns $134,499.
Top markets for the job
New York ranks highly among all other regions in the world not only in machine learning engineering jobs but also in most of top AI jobs. The high efficiency and performance of banking and fashion industries in New York City are attributed to the concentration of AI jobs.
San Francisco, San Jose, Washington DC, Boston, and Seattle follow New York City in that order
Machine-Learning engineering is a top career in the world. Gaining skills and securing an opportunity in a reputable company are two different things. You have to be good. Here is what you need when going for an interview:
- Proof of current or previous project
- Familiarity and proficiency with machine learning tools
- Knowledge of handling corrupted data and cleaning data
- Understanding of ethical standards in machine learning
The digital era offers endless paths to a career in machine learning engineering. You can quickly start by learning Python, Java, or R then advance to obtain certifications through MOOCs. After gaining enough skills, you can proceed to design projects and to interact with peers or professionals in the field to help you grow.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.