6 Productionizing ML Models
This chapter covers
- Deploy ML models as a service using the BentoML deployment manager
- Track data drift using Evidently
In the last chapter, we learnt how to orchestrate ML pipelines in Kubeflow. Kubeflow pipelines are a powerful tool that can help you build, scale and manage machine learning pipelines.
This chapter delves into the crucial post-training phases of a machine learning model's life cycle: deployment and monitoring. We explore how to efficiently serve models as APIs using BentoML, a powerful platform that simplifies the deployment process and reduces reliance on complex infrastructure setup. Additionally, we tackle the challenge of data drift – a common phenomenon that can degrade model performance over time. We introduce Evidently, a tool designed to detect and analyze data drift, enabling us to take corrective actions and maintain model accuracy in real-world scenarios.
Through practical examples and step-by-step guides, this chapter equips you with the knowledge and tools to confidently deploy and monitor your models, ensuring their long-term effectiveness and value.