As a Data Engineer at DataCore Solutions, I've frequently used both SQL and NoSQL databases within various machine learning projects. A recent initiative involved creating a predictive analytics model for a client in the retail industry, and for this, we chose to use a MySQL database.
With SQL's powerful query language and relational structure, we were able to efficiently join multiple tables and clean our data for training the model. I utilized tools like Python's SQLAlchemy and Pandas libraries to interface with the database, perform queries, and manipulate the data.
Another project involved dealing with large volumes of unstructured customer review data. For this, we opted for a MongoDB NoSQL solution, which allowed us to store and process multiform data more effectively. This project demonstrated the significant import of NoSQL when working with big data and machine learning.
Both projects resulted in successful models and satisfied clients, which underscores the importance of selecting the right database system based on the specific characteristics and requirements of the ML project.