Indeed recently concluded that the position of a Machine Learning Engineer (MLE) is the best job to have in 2019, citing the 344% growth in the postings for the job since 2015 as the primary reason.
A similar trend has trickled down home, as well. According to a LinkedIn report, the role of an MLE is the fastest growing job in India. The role has grown 43 times in the past five years in India.
As AI takes the forefront of technological development for more and more companies – both big and small, it is obvious that roles involving artificial intelligence and automation are will continue to grow from strength to strength.
Development in software engineering is all about the skill to automate tasks. Software Engineers develop programs that would instruct a computer to execute a task on its own instead of doing it manually. Machine learning is literally the next step, where you automate the automation. Interesting right? Here’s what this really means.
Who is an MLE?
To understand the role of an MLE, it is important to know what machine learning means.
Machine learning is a subset of artificial intelligence which allows computers (machines) to automatically learn and improve from experience. Arthur Samuel who actually had coined the term, Machine Learning, defines it as:
“Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.”
A great example to understand this better is the Netflix recommendation system. Before we finish binging on a particular TV show, Netflix is ready to suggest the next movie or show we should watch. This is where machine learning comes into play.
If it were left to traditional programming, then an engineer would have to write a code for each and every clipping/show/ film on Netflix- “if a user watches ‘X’ show, recommend A, B, C, D to him.” Considering the vast amount of content Netflix has, the number of new users added every day and the impossibility for one engineer to have the right intuition to do this, traditional programming would not have allowed for such a system to exist. But it does. How? Machine Learning.
Netflix uses a machine learning algorithm that takes input from the user itself, learns their tastes and choices and then recommends similar content. For those who are new to the platform, it recommends content that people with similar to you have watched. Basically, Netflix searches from movies or series similar to ones you or people like you have interacted with.
New users on Netflix are a cold start for them. Traditional programming won’t allow them to give recommendations to these users, but machine learning does.
Oleksii Kharkovyna, an AI specialist, sums this up well-
“In traditional programming you hard code the behaviour of the program. In machine learning, you leave a lot of that to the machine to learn from data.”
Machine Learning Engineer
According to Andrew Zola , an MLE’s job is to develop algorithms that can receive input data and leverage statistical models to predict an output while updating outputs as new data becomes available.
The role of an MLE is therefore at the junction of a software engineer and a data scientist. While data scientists help them in building statistical models to predict outputs, MLEs use their programming skills to build algorithms that can leverage these models and update themselves based on new data. In other words, an MLE deploys the models into production.
No wonder, they are much sought after these days. So what knowledge and skill set makes an MLE?
Skills required to become an MLE
Python and R
Being a simple language, Python is undisputedly the most popular language among MLEs. This popularity is credited to the fact that Python is not difficult to learn and is very user-friendly. To top that, it is versatile enough to be used on any platform such as Windows, MacOS, Linux, Unix, etc. Sound knowledge of Python aids you while working with different data science and data engineering frameworks. R is a statistical programming software provides techniques for data analysis, visualization, sampling, supervised learning and model evaluation and hence comes handy.
Algorithms and Libraries
An MLE needs to be familiar with different algorithms to produce machine learning programs. For instance, algorithms associated with SVMs (support vector machines), linear and logistic regressions, classification, etc. Deep learning is yet another subset of machine learning that you must be accustomed to. It has become the next big thing in machine learning. Deep learning involves learning methods based on artificial neural networks. And just the knowledge of these algorithms and methods is not enough. You must understand their application as well so that you can choose when to deploy which algorithm.
For handling such algorithms, you can use ML libraries. Some of the most popular libraries used in ML are
- Keras– Helps you with fast calculations and prototyping used in deep learning.
- Tensorflow– Helps in setting up, training and using artificial neural networks with massive data sets, used in deep learning
- Scikit-learn– Help you with the basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
Hadoop Ecosystem is an asset for a data scientist as well as an MLE. It is an open-source, free-to-use framework that allows distributed processing of large data sets across clusters of computers. The Hadoop ecosystem has many tools that help manage, ingest, store, analyse and maintain your data.
GIT and Github
As an MLE, being able to collaborate with large teams is essential. Git and Github come in handy while collaborating across different codebases and across different models quickly with different teams.
MLE is a deep-tech job but there are certain non-tech skills that would come handy. Good communication skills, strong business acumen, insightfulness and problem-solving ability sometimes help recruiters pick out an exceptional MLE candidate from an average one.
Apart from all these skills, what really sets an MLE apart is their knowledge of recent developments in the technological field. For instance, presently, deep learning is a hotbed for MLEs. Machine learning has something new to offer every day. And being up-to-date on development in the tools, theory and algorithms (research papers, blogs, conference videos, etc.) helps.
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