ML Engineering: past and current industry trends, open-source and what the future looks like for the ML engineer.
By: Arnab Borah
Tags: Industry, Machine Learning, Trends, 2020, Deep Learning

A brief history of ML positions over time and the usual talent pool

Photo by Carles Rabada on Unsplash

Historically, companies thrived on having a smart data-science group, and top tech companies would compete with each other for the best talent. Speaking with my observation of the hiring trends in the bay area and the US in general, most of this talent pool usually referred to people with a proven research record. This is generally true for big tech companies all over the world. When you take a look at the type of positions in ML at top companies and talent pool, we can divide recent history into these.

Pre-TensorFlow era (before 2015)

In this era, data science has evolved well enough, and almost all tech companies leading the industry teams have strong data science teams of their own. And as mentioned before, their strength lies in their data-scientists. But from an engineering point of view, ‘cloud’ was the buzzword and almost all major companies have their product/service on cloud ecosystems, either of their own or on AWS, with rising Azure and Google Cloud, and cloud service providers hiring a large number of software engineers. DevOps had seen a huge growth.

When you talk about a data-scientist position straight after college, they used to be open to a select few with STEM PhD’s or MS students with good relevant publications (NIPS, KDD, etc), i.e. companies were mostly looking for candidates with a proven track record in research to work on their ML problems. The bar was high.

Data-science teams usually comprise of Data-scientists and Software-engineers working together with other engineers (dev, quality, devops). Data-engineers usually used to belong to a specific team (platform team, data-engineering-groups etc) serving data-engineering needs of many different orgs, and only if the org is a big one they have their own data-engineering team. There usually weren’t any specialized ‘Machine Learning’ Engineer (MLE’s at most companies. Mostly it was ‘Software Engineers’ who did ML, in collaboration with data-scientists and product managers, and engineers from other non-ML teams. And, that’s in short how usually they functioned. At smaller companies/startups a data-scientist would generally do a lot of these things themselves (research, product management, engineering, data engineering etc).

Deep-learning mass adaption era (2016–2019)

By now (2016), most of the big companies (tech and non-tech) have started adapting ML/deep learning frameworks at some scale to optimize something somewhere in their pipelines, accompanied by changes in their traditional infrastructure to be able to do that. Many who started new ML teams, did so entirely on cloud. This happened mostly because of these following reasons.

Tensorflow, PyTorch, etc and their widespread adaption across all companies because of improvement in GPU architecture and decreasing cost of compute. That along with widespread availability of free cloud based compute power enabling deep-learning academic research, brought in some revolutionary new academic research during this age, along with revolutionary deep learning work being made available to use. A boom in self-driving startups boosting research in computer vision and applied deep learning. This of course led by the likes of Tesla, Uber, and hundreds of AI-startups working in the space many funded by big Auto and big Tech sometimes in partnership. There are couple of dozens of car companies apart from Tesla working on a production electric car to deliver in a few years. A boom in adaptation and interest in conversational-AI (which seemed to have hyped down over years) boosting academic research as well as adaptation of conversational dialogue systems in products across the system to interact with customers/users. This age saw the rise of a new kind of products AI-enabled-speakers, which ran Alexa, Cortana, Siri, Google Assistant etc. All major device maker basically had their own AI-chatbot, etc, resulting in the revolution of the field of Natural Language Processing/Understanding. Similar booms occurred, in multiple different sub-fields, all taking deep-learning a step forward. Breakthrough inventions in deep learning from top research orgs and researchers, made AI researchers at top universities attain the status of top basketball players in tech. Many work part time or moved full time to lead tech labs of big Tech, while others continued to produce tremendous academic research, mostly funded by corporations themselves involved in AI-research. This age strengthened and increased the academia-industry tie in ML research. Many ML products we use today, are results of industry-academia partnerships. The Generative Adversarial Networks (GAN’s) generation: Most of the breakthrough research happened during this phase but I guess I’d get called out if I do not mention GAN’s, and the countless problems it had solved. GAN today is a new subject/topic altogether with full-fedged university classes dedicated to GAN’s. GAN’s came before 2016 actually, (2014) but this was the time when GAN’s attained widespread popularity. Apart from GAN’S all major deep learning tech became really common especially driven by advancements in computer vision (CNN, R-CNN, etc), also RNN’s, LSTM’s, Auto-encoders and others.

…and many other events actually which shaped the ML landscape of today. I certainly missed a lot of other key events…