MLFLow is an open source MLOps tool that can be used to deliver enterprise level tracking and productionisation of AI systems. While MLFlow is fully compatible with deployments on the cloud we will be presenting a localhost flow using MLFlow automated tracking, Projects, Models and Registry. These components can be either used on an isolated basis or as a framework able to productionise an end-to-end MLOps process.
The source code presented in this project has been written by Refinitiv only for the purpose of illustrating the concepts of creating example scenarios using the Refinitiv Data Library for Python.
Note: To ask questions and benefit from the learning material, I recommend you to register on the Refinitiv Developer Community
To execute any workbook, refer to the following:
- A Refinitiv Desktop license (Refinitiv Eikon or Refinitiv Workspace) that has API access
- Tested with Python 3.7.13
- Packages: mlflow, refinitiv.data
- RD Library for Python installation: 'pip install refinitiv-data'
- Marios Skevofylakas