The recent ODSC conference highlighted changes in data science in the last few years including the move to cloud, the need to support increasingly sophisticated workflows, and more attention on security.
RTInsights recently had the opportunity to speak with Sheamus McGovern, Founder and CEO of the Open Data Science Conference (ODSC). ODSC is one of the leading, most comprehensive conference and training organizations dedicated to data science. It brings together experts from the tech industry, academia, and government organizations and a cross-section of data scientists with the various professionals that support AI and big data solutions.
How have you seen the field of data science evolve in the years since you founded ODSC in 2015?
McGovern: I would say definitely the biggest shift since 2015 has been the move off the laptop and onto the cloud. What that really meant was the scale of data science and machine learning could really expand over the last five years. Before, people were working on what we would call small to medium-sized data sets with smaller feature sets. Back then, if you were dealing with 10,000 features, you would think that is a massive data set. Just working with ten features was a challenge.
Data science took a different path to cloud than software development because software development went to the cloud for the purposes of productivity and certainly scalability, but machine learning went there because it was imperative in order to scale within the machine learning workflow.
Now that data science is firmly linked to cloud capabilities and has crossed new scale boundaries. Where are you seeing the most change now?
McGovern: Over the last three years, we’ve seen a practice evolve that was searching for a label. We were calling it data ops, AI ops, and dev data ops. And then finally, in the last two years, it became known as MLOps.
Related: 6Q4: Demetrios Brinkmann, on the role of community in solving MLOps’ greatest challenges
Once data science moved to the cloud, the workflows got more sophisticated. So, your workflow had to cover feature engineering, feature modeling, feature deprecation, monitoring, etc. In addition, because you’ve gone to the cloud now, machine learning and data science were catching up with software architecture and what the software field was doing in terms of DevOps.
And now, we’re starting to pay attention to continuous integration, continuous monitoring, and real-time monitoring of models and applications. The workflow now encompasses the whole range of real-time event-processing, data analytics, data science, and machine learning. These were emerging on different paths, but now you see software engineering, data engineering, and data science machine learning starting to converge–primarily because they moved to the cloud.
Would you say that data science is becoming a truly interdisciplinary endeavor?
McGovern: Right. When data scientists were working away on their laptops, they could forget about a lot of the dependencies. Now everything is converging because of the cloud.
How does that affect the traditional role of a data scientist, if there ever was such a thing as a traditional role?
McGovern: When we hear that someone is hiring a data scientist, we ask ourselves, what does that really mean? There’s even a big difference between the related roles of a data scientist and a machine learning engineer. It always seemed …….
Source: https://www.rtinsights.com/the-evolution-of-data-science-and-its-changing-role-evident-at-odsc/