The job title “ML Engineer” is quickly outpacing “Data Scientist” in the new decade. Here are five reasons why you may want to become an ML engineer.
With the rapid growth of artificial intelligence comes a rising demand for Machine Learning (ML) engineers. AI-driven software that employs deep learning, Machine Learning, voice AI, autonomous machines, and machine vision are but a few of the drivers.
Another factor driving the rise of ML engineers is the deficit of experienced data scientists. As a result, many companies have already realized that, much like software development, it’s best to spread the work across several roles.
The ML engineer role lies between software engineering and data science. In larger teams, ML engineers free up data scientists to focus on core modeling that requires deep scientific expertise,such as statistics or other forms of mathematical modeling,leaving the engineering side to ML engineers.
What Exactly is an ML Engineer?
A quick search for “Machine Learning Engineer” on a job board will show you how skills and experience are prioritized under “Prefered Qualifications,” with qualifications such as a computer science or engineering background, coding skills, and machine learning framework experience included. Mathematical modeling skills, on the other hand, are listed, but often not prioritized.
I’ve seen descriptions of the differences between ML engineers and data scientists that range from quite good to just plain wrong, notwithstanding the fact that many companies use the terms ML engineer and data scientist interchangeably. I propose a somewhat simple definition of a data scientist:
If you can code and build unique, usable, accurate models from scratch then you are a data scientist.
On the other hand, what is an ML engineer? That requires a little more context and an understanding of contributing trends.
Machine learning and deep learning frameworks form much of the infrastructure and do most of the heavy lifting in the data science ecosystem. In the past five years, there has been a slew of frameworks released. Programming languages such as Python, R, Julia, and even Java have many libraries and packages specific to ML and DL. However, it’s the open-source availability and ease of use of the more powerful and feature-rich machine learning and deep learning frameworks, such as TensorFlow, PyTorch, Keras, and Spark that allow the role of the ML engineer to thrive. Expertise in at least some of these popular frameworks is a key requirement for the role.
We’ve come a long way since the Iris data set. Pre-trained models are becoming more readily available. These models were widely adopted in deep learning networks, such as YOLO and Mask R-CNN, for bounding boxes in image detection and VGG-Face and FaceNet for facial recognition.
The same trend continues with natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG). Pre-trained models are making intelligent chatbots, Q&A system, language translation, and many more NLP applications readily accessible. Some of the well-known multi-purpose pre-trained models include BERT, GPT-2, UMLFIT and especially the Hugging Face Transformers API library, which gives ready access to 32+ pretrained NLU and NLG models. In addition, libraries like spaCy provide core general-purpose pre-trained models capable of predicting named entities, part-of-speech tags and syntactic dependencies.
