BENEFITS OF CHOOSING MACHINE LEARNING AS A CAREER

To say that machine learning is a growing field would be an understatement.

Here's some perspective:

According to a report by leading job site Indeed, Machine Learning Engineers are in high demand with career opportunities with an average salary of $146,085, a growth rate of 344 percent over the last year.

Machine learning now has a lot of applications in all industries, which is the primary reason why there is a high demand for jobs in that field. If you have been waiting, now is the right time to consider making a career in ML & AI.

Why Choose Machine Learning as a Career in 2022

Machine learning is the fuel we need to power AI as well as robots.

With ML, we can power programs that can be easily updated and modified to adapt to new environments and tasks—to get things done quickly and efficiently.

Here are some reasons to pursue a career in ML:

1. ML is the skill of the future - Despite the exponential growth in machine learning, there is a skill shortage in the field. If you can meet the demands of large companies by specializing in ML, you will have a secure career in technology that is on the rise.

2. Work on real challenges - In this digital age businesses face a lot of issues that ML promises to solve. As an ML Engineer, you will work on real-life challenges and develop solutions that have a profound impact on how businesses and people thrive. Needless to say, a job that allows you to work and resolve real-world conflicts gives high satisfaction.

3. Learn and Grow - Since ML is booming, by entering the field early, you can spot trends first and increase your relevance in the market, thus increasing your value to your employer.

4. An Exponential Career Graph - Said and done, machine learning is still in its infancy. And as the technology matures and advances, you'll have the experience and expertise to follow an upward career graph and contact your ideal employers.

5. Build a lucrative career- The average salary of an ML engineer is one of the top reasons why ML seems to be a lucrative career for many of us. Since the industry is growing, this figure can be expected to increase as the years go by.

6. Side-Step in Data Science - Machine Learning skills to help you expand your career. ML skills can provide you with two hats- the other being that of a data scientist. Become a hot resource by specializing in both the fields simultaneously and embark on an exciting journey full of challenges, opportunities and knowledge.

Read more: Machine Learning As A Service is Redefining The Businesses In New A Light!

Machine learning is happening right now. So, you want to get an early bird advantage of messing around with the solutions and technologies that support it. That way, when the time comes, you will find your skills in high demand and be able to secure an ever-growing career path.

What will be the career path in Machine Learning

The machine learning career path usually begins as a machine learning engineer. Machine learning engineers develop applications and solutions that automate common tasks previously handled by humans. Most of these are repetitive tasks based on condition and action pairs—which machines can perform efficiently without error.

When you earn a promotion as an ML Engineer, you are on your way to becoming an ML Architect. People in this role develop and design prototypes for applications that need to be developed.

Some of the other roles available in the field are ML Data Scientist, ML Software Engineer, Senior Architect, and so on.

A software engineer who has sufficient knowledge of Python and Core ML libraries can switch careers to ML.

Here are some other technical areas that help when known by an ML professional:

1. Probability and Statistics - A lot of ML algorithms have their basis in Bayes rules, Markov models and other probability procedures. There are also data- mean, median, deviation, Poisson distribution, etc.

2. System Design - ML solutions are rarely standalone products. Mostly, these are part of an integrated technological ecosystem. Hence, it helps ML professionals to have a sound knowledge of software design.

3. ML Algorithms and Libraries - Knowledge of models like Linear Regression, Bagging, Boosting and Genetic Algorithms proves useful for ML professionals.

4. Data Modeling - As an ML practitioner, you need to be able to predict the structure of datasets to find patterns, clusters and correlations. Data models require constant evaluation to make sure they are on point. Additionally, you should also know how the data being evaluated is tested for accuracy and completeness.

5.  Programming Languages ​​- Python is an important programming language for anyone looking to make a career in ML. Apache Spark is another technology, followed by SAS.

This is not a comprehensive list that can be done once and then done with. Candidates need to be on their toes, and always be active in upgrading their skills and knowledge if they want to create an upward career graph.

Read more: Is Machine Learning a Good Career?

Building a career in ML can help you be an active part of the digital revolution we talk about in sectors ranging from healthcare to retail, logistics, manufacturing, etc. Having ML skills makes you a hot resource in any field, which leaves a lot of avenues open for you to choose from. This way, you are in complete control of your career as an ML professional. If you are interested in pursuing a career in machine learning, take a look at our machine learning program.