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Theodore Meynard - MLflow workshop | PyData London 2023
Machine learning model deployment and real-time iteration with MLflow and Airflow, overcoming challenges in accuracy, latency, and infrastructure scalability, and building a robust system with error detection and customization.
- The speaker faced challenges in deploying a machine learning model to production, including handling user signals, data sets, and model changes.
- The team used MLflow for model training and deployment, and Airflow for orchestration and automation.
- The speaker emphasized the importance of having a clear interface (e.g., MLflow) and being able to analyze and iterate on the model in real-time.
- The team faced issues with model accuracy and latency, and had to implement solutions such as incremental model updates and health checks.
- The speaker discussed the importance of having a scalable and fault-tolerant infrastructure (e.g., Kubernetes) to handle model deployment and inference.
- The team used sampling data to validate and test the model, and then deployed it to production using MLflow and Airflow.
- The speaker highlighted the challenges of handling model changes and updates in production, and emphasized the importance of having a system in place to detect and handle errors.
- The team used Argo for deployment and rollback of the model, and Airflow for orchestration and automation.
- The speaker mentioned the importance of having a clear understanding of the user signals and data sets used in the model, and the need for customization and personalization.
- The team used Databricks for data processing and analysis, and MLflow for model training and deployment.
- The speaker discussed the importance of having a strong infrastructure and automation in place to handle model deployment and inference, and highlighted the challenges of doing so.
- The team used an open-source platform (e.g., MLflow) and Kubernetes for scalability and fault tolerance.
- The speaker emphasized the importance of having a clear understanding of the model’s performance and limitations.
- The team used incremental model updates and health checks to ensure the model’s performance and accuracy.
- The speaker discussed the importance of having a system in place to detect and handle errors, and highlighted the challenges of handling model changes and updates in production.