|Map > Problem Definition > Data Preparation > Data Exploration > Modeling > Evaluation > Deployment|
|The concept of deployment in data science refers to the application of a model for prediction using a new data. Building a model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that can be used efficiently. Depending on the requirements, the deployment phase can be as simple as generating a SQL or Python code or as complex as implementing a web-based platform.|
Federated learning is a learning paradigm seeking to address the problem of data governance and privacy by training algorithms collaboratively without exchanging the data itself. Federated learning is very important when we decide to deploy predictive models in the field of healthcare.
|Watch this video to learn how you can deploy predictive models using Bioada SmartArray significantly faster and easier. Bioada platform supports federated learning.|