ML development brings many new complexities beyond the traditional software development lifecycle. ML projects, unlike software projects, after they were successfully delivered and deployed, cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements. In most ML use cases, we have to deal with updates of our training set, which can influence model performance. In addition, most models require certain data pre- and post-processing in runtime, which makes the deployment process even more challenging. In this talk, we will show how MLflow can be used to build an automated CI/CD pipeline that can deploy a new version of the model and code around it to production. In addition, we will show how the same approach can be used in the data training pipeline that will retrain model on arrival of new data and deploy the new version of the model if it satisfies all requirements.
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- Continuous Delivery of ML-Enabled Pipelines on Databricks using MLflow ( Download)
- CI/CD Templates: Continuous Delivery of ML-Enabled Data Pipelines on Databricks ( Download)
- Developing ML-enabled Data Pipelines on Databricks using IDE & CI/CD at Runtastic ( Download)
- Databricks MLOps With GitHub Actions & MLflow ( Download)
- Accelerating MLflow 2.0 Pipelines for Production using Databricks ( Download)
- Navigating the ML Pipeline Jungle with MLflow: Notes from the Field with Thunder Shiviah -Databricks ( Download)
- MLflow Pipelines: Accelerating MLOps from Development to Production ( Download)
- MLOps Using MLflow ( Download)
- How Outreach.io Runs Continuous integration and Continuous Delivery of ML Models ( Download)
- Databricks CI/CD: Intro to Databricks Asset Bundles (DABs) ( Download)
- Trigger MLOps Pipelines using Databricks MLflow Webhooks ( Download)
- MLflow Pipelines: Accelerating MLOps from Development to Production - Databricks Summit 2022 ( Download)
- Databricks Dev Ops ( Download)
- MLOps on Databricks: A How-To Guide ( Download)
- Productionalizing Models through CI/CD Design with MLflow ( Download)
