To state that Machine learning is a growing field would be an understatement.
Here’s some perspective:
According to a report from the leading job site Indeed, machine learning engineers are in high demand with the opportunity of a career boasting an average salary of $146,085 at a growth rate of 344 per cent from last year.
All industries now have a multitude of applications in machine learning, which is the primary reason why there is a high demand for jobs in that field. If you’ve been waiting, now is the right time to consider pursuing a career in ML.
Why Pursue a Career in Machine Learning in 2019?
Machine learning is the fuel we need to power robots, alongside AI.
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 a few reasons for you to pursue a career in ML:
– ML is a skill of the future – Despite the exponential growth in machine learning, the field faces skill shortage. If you can meet the demands of large companies by gaining expertise in ML, you will have a secure career in a technology which is on the rise.
– Work on real challenges – Businesses in this digital age 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 deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world struggles gives high satisfaction.
– Learn and grow – Since ML is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer.
– An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have experience and expertise to follow an upward career graph and approach your ideal employers.
– Build a lucrative career– The average salary of an ML engineer is one of the top reasons why ML seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by.
– Side-step into data science – Machine learning skills help you expand avenues in your career. ML skills can endow you with two hats- the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge.
Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise.
What Does the Career Path in Machine Learning Look Like?
A 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 without errors, efficiently.
When you earn a promotion as an ML engineer, you step onto being an ML Architect. People in this role develop and design prototypes for applications that need developing.
A few other roles available in the field are ML data scientist, ML software engineer, senior architect, and so on.
A software engineer with enough knowledge of Python and the core ML libraries can switch careers into ML.
Here are a few other tech areas that help if known by an ML professional:
– Probability and Statistics – A lot of ML algorithms have their base in Bayes rule, Markov models, and other probability processes. There’s also statistics- mean, median, deviation, Poisson distribution, and so on.
– System Design – ML solutions are rarely standalone products. Mostly, these are part of an integrated tech ecosystem. Therefore, it helps ML professionals to have sound knowledge of software design.
– ML Algorithms and Libraries – Having knowledge of models such as Linear Regression, Bagging, Boosting, and Genetic algorithms prove useful for ML professionals.
– Data Modeling – As an ML practitioner, you should be capable of estimating the structure of a dataset to find patterns, cluster, and correlations. Data models also need continuous evaluation to ensure they are on point. Additionally, you should even know how to test the data that is being evaluated for accuracy and completeness.
– Programming Languages – Python is a crucial programming language for anyone looking to build a career in ML. Apache Spark is another technology, followed by SAS.
This is not a comprehensive list which can be undertaken once and then is done with. Aspirants need to be on their toes, always proactive in upgrading their skills and knowledge if they want to pursue an upward career graph.
Pursuing 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, and so on. Having ML skills makes you a hot resource in any sector, which leaves a lot of open avenues for you to choose from. This way, you are in total control of your career as an ML professional. Take a look at our Machine Learning program if you’re further interested in building a career in Machine Learning.