In this talk, we focus on the impact of model versioning on stable and reliable MLOps. During the talk, we will demonstrate two MLOps pipelines; one with a model versioning solution as its foundation and one without, both using Jenkins for building and delivery and Prometheus/Grafana for monitoring. Through a few real-world simulations, we will show how a robust model versioning system can enable fast remediations of incidents and ensure that the MLOps pipeline can run reliably. We will wrap-up with a few best practices on building an MLOps pipeline using open-source components.
- What does an MLOps stack look like?
- How to build an MLOps pipeline with open-source technologies namely ModelDB, Jenkins and Prometheus?
- Why is versioning key to robust operations?
- Basic understanding of DevOps toolchain including Git, Jenkins, and optionally Prometheus
- Attendees should have some experience building models
In a field that is rapidly evolving but lacks infrastructure to operationalize and govern models, ModelDB 2.0 provides the ability to version the full modeling process including the underlying data and training configurations, ensuring that teams can always go back and re-create a model, whether to remedy a production incident or to answer a regulatory query.
- Layered API-focused client: easy extension of functionality and integration with frameworks
- Integration with popular ML frameworks
- Artifact management: reliably track the result of the training process
- Git-based versioning for all components for a model
- Single pane of glass for a company’s model development
- User management support for authentication, RBAC authorization and workspace isolation
Models are the new code. While machine learning models are increasingly being used to make critical product and business decisions, the process of developing and deploying ML models remain ad-hoc. In the “wild-west” of data science and ML tools, versioning, management, and deployment of models are massive hurdles in making ML efforts successful. As creators of ModelDB, an open-source model management solution developed at MIT CSAIL, we have helped manage and deploy a host of models ranging from cutting-edge deep learning models to traditional ML models in finance. In each of these applications, we have found that the key to enabling production ML is an often-overlooked but critical step: model versioning. Without a means to uniquely identify, reproduce, or rollback a model, production ML pipelines remain brittle and unreliable.
In this webinar, we draw upon our experience with ModelDB and Verta to present best practices and tools for model versioning and how having a robust versioning solution (akin to Git for code) can streamlining DS/ML, enable rapid deployment, and ensure high quality of deployed ML models.