There is absolutely no denying that machine learning is the future especially with the explosion in data that we see around us. Simply defined, machine learning the process of using AI to ‘learn’ from existing data to make decisions with minimal human interaction or explicit programming.
There are plenty of use cases for machine learning that we see across industries in our day to day lives – from your bank’s chatbot guiding you on the best investment options for you to your sales tool zeroing down on the most promising prospect who is most likely to convert.
What does a Machine Learning Engineer do?
In many ways, a machine learning engineer is a lot like a programmer. The primary difference is the machine learning expert needs to create programs that enable machines to self-learn and produce results without human intervention. In general, there are a variety of roles that a machine learning engineer might play.
For instance, it could be about building an algorithm that reviews large sets of data and identifies trends and patterns based on it. For example, Amazon can direct notifications to buyers based on their purchasing and browsing history of a user. Other possible tasks include producing project outcomes and isolating issues that require resolution, to make the programs more effective; building data and model pipelines; and managing the infrastructure and data pipelines that are necessary for bringing code to production.
Their role could also constitute using data modeling and evaluation strategies to find patterns and predict unseen instances; building algorithms based on statistical modeling procedures; and building and maintaining scalable machine learning solutions in production.
The applications for machine learning are aggressively expanding, be it in software like Siri or the automobile sector for driverless vehicle testing, machine learning is becoming the future in many industries. Machine learning is now actively being used where decision making has to be in real time and based on large sets of data. This is a realm where human decision making falls short.
As an example, It’s widely employed by social media networks to create a more personalized, enjoyable experience for social media users. Another example where machine learning technology is being leveraged is in the healthcare field to help improve patient care and help avoid lapses that occur due to human error. It allows doctors to use diagnostic tests and equipment more efficiently to detect diseases such as early-stage cancer that often go unnoticed in the beginning stages. Machine learning applications are far and wide.
Not surprising then that machine Learning is seen as one of the most trending careers right now. It is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022 as widely known industries are integrating machine learning with their software. According to Gartner, AI and ML will create 2.3 million jobs in 2020 that include research and developing algorithms. Machine learning Engineers will have to extract patterns from Big Data too. Mastering the art of Machine learning can lead you to roles such as Machine Learning Engineer, Data Sciences Lead, Machine Learning Scientist, or NLP Data Scientist.
Whether you’re a data scientist or a software developer, a certification program on machine learning could get you into an exciting new role across industries especially since learning on the job is relatively harder for such a niche skill. AI-related job postings have increased by well over 100% on top career sites such as Indeed.com. Of the most in-demand AI-related careers, machine learning capabilities ranked in the top 3 of the highest sought-after skills, creating millions of job opportunities in the future.
For professionals who are keen to pursue a career in machine learning, there are a few areas or topics that are certainly worth your while. Here are a few areas that one must focus on:
* Programming: Learn a relevant language such as Python and develop additional programming skills in R, Java, SQL Scala and, etc.
* Data and Statistics: Develop an understanding of data analysis, data modeling, statistics, and probability, along with some basic data visualization skills
No matter which way you look at it, machine learning is here to stay. For software professionals, getting trained in machine learning is an important way to future-proof your career, improve your prospects, and be a harbinger of change.
(Author Anand Narayanan is Chief Product Officer, Simplilearn. Views expressed here are personal.)
Credit: Google News