MongoDB is a NoSQL database that supports a wide variety of input datasets. It comes with the ability to seamlessly modify parameters for model training. Data Scientists can easily combine the structuring of data with model generation. It can also be used for high-value data storage. With its rich programming, it is a highly recommended method of deploying APIs.
Useful Features of MongoDB in Machine Learning
Parallelization and Models
For the purposes of high parallelization of data processing across a distributed database, MongoDB provides the aggregation pipeline and MapReduce techniques. NoSQL is already a great way to enhance the database experience. MongoDB is based on the JSON Documents. It can be used to store models as well.
Dynamic analysis capabilities
MongoDB’s dynamic nature enables its usage in database manipulation tasks in Machine Learning applications. It is an efficient and easy way to carry out an analysis of datasets and databases. The output of the analysis can be used in training machine learning models. It has been recommended that data analysts and ML programmers gain mastery in MongoDB to apply it in many different applications. MongoDB’s Aggregation framework is used for data science workflow for performing data analysis for numerous applications.
Saving models in a database and loading them, using python, is also an easy and much-required method. Choosing MongoDB is also beneficial as it is an open-source document database and also a leading NoSQL database. MongoDB also serves as a connector for apache spark distributed framework.
Library and Features
PyMongo is a great library to embed MongoDB syntax in Python code. We can import all the functions and methods of MongoDB to use them in our machine learning code. It is a great technique to get multi-language functionality in a single code. The additional advantage is that you can use the essential features of those programming languages to create an efficient application.
We can install Pymongo with the help of pip install pymongo command through the command terminal. On the operations side, there are quite a few tools and features for MongoDB that you hardly find in other database systems. You can easily include Mongo documents in your Jupyter notebooks or any machine learning code with the help of PyMongo DB library.
There are multiple courses on MOOCs like udemy.com and edx.com for learning this database technology. As machine learning applications are becoming more complex, there is an increase in cross-platform programming for faster and efficient results. MongoDB is one of the tools that you will find quite handy as a developer.