For machine learning to be used more widely, it needs to be brought closer to the data that fuels ML modeling and insights. For database developers and data analysts that are still getting up to speed on ML modeling, the ideal scenario is to integrate ML algorithms and training models directly into the tools they already use, making it easier for them to extract meaningful insights from their data.
In-house database teams are likely to be experts in SQL, but they may not know Python, which has emerged as a primary programming language for AI and machine learning. As a result, they’re reliant on data scientists to build the models for them to add intelligence to their applications.
