It uncovers the specific challenges in developing ML model-based solutions, highlighting the importance of integrating data scientist profiles and the collaboration between multidisciplinary teams. It will help you provide strategies and tools to implement MLOps effectively, dividing responsibilities, promoting iterative testing and ensuring agile and reliable processes in the development and deployment of ML models.
MLOps is an essential resource for improving business competitiveness by incorporating ML models in an organized approach, enabling continuous improvement and rapidly exploring new business opportunities.
INDEX
1. Introduction
2. What is MLOps?
3. What challenges are addressed with the implementation of MLOps?
4. How to implement MLOps?
5. The benefits of MLOps: division of responsibilities
6. Other benefits of MLOps
7. Tools to implement MLOps
8. Conclusions