Summary
In this high-level introduction to MLOps, a crucial discipline in the AI and data science landscape, we delved into its key aspects. We began by understanding the significance of MLOps, its role in bridging the gap between model development and production deployment, and the impact of a well-structured MLOps pipeline on business outcomes.
The chapter covered the MLOps journey, emphasizing the importance of reproducibility, collaboration, and automation in the ML workflow. We explored developing model pipelines, technologies such as Docker and Databricks, and model versioning. Additionally, we discussed the cloud-native tools and services available to manage ML experiments and monitor model performance. Finally, we examined governance and compliance practices in AI, ensuring ethical and regulatory alignment.
This chapter serves as a roadmap for implementing MLOps best practices, enabling organizations to develop, deploy, and manage ML solutions efficiently and responsibly...
