How do you get data scientists and data engineers to work together? While their objectives are the same, their differing focus can make productive collaboration daunting. And if not handled properly, this can culminate in unnecessary project delays.
As observed by data science list Maxwell Boyd in a post on VentureBeat, data scientists are focused on building the most accurate models and manipulating data in sophisticated ways. On the other hand, data engineers want to build reliable, highly efficient systems to crunch the numbers.
To succeed, Boyd suggested focusing on the common denominator: their dedication towards timely and quality information. This starts with acknowledging that both groups are immensely valuable and establishing a collaborative environment for both to work together.
And yes, emerging tools, frameworks, and new methodologies can also help bridge the gap between both camps – as well as save valuable time by making it easy to monitor and test data. A couple of tools Boyd highlighted include Great Expectations to help improve how databases are built and monitored, and Databand.ai for data pipeline monitoring.
Boyd recommends that organizations look to MLOps to implement continuous integration (CI) and continuous delivery (CD) for ML systems. This approach lifts the burden of building and maintenance from data engineers, he says, even as it offers flexibility and freedom for data scientists for a win-win solution.
On this front, new open-source systems such as MLFlow and KubeFlow can be leveraged to streamline model deployment; both these tools rely on containerization to facilitate model deployment to ease the work of the data engineers on the infrastructure side of things.
Finally, do consider feature stores, which are data stores built to support the training and ease of bringing machine learning models into the production environment. This gives data scientists the peace of mind while making things simpler for data engineers to maintain.
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