We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Carlos Villavieja & Salim Virji – Productionizing ML
Learn how to productionize machine learning models by focusing on data quality, observability, and collaboration. Explore best practices for training, validation, deployment, and monitoring of models to ensure scalability and reliability.
- Productionizing ML requires attention to data quality, observability, and collaboration between teams.
- Training and validation of models are crucial and should be done in a pipeline that can confirm expected outputs.
- Hyperparameters can impact model performance, and validation should be done on multiple models.
- Ensure data quality by implementing data imputation, filtering, and normalization.
- Use automation and CI/CD pipelines to streamline the machine learning workflow.
- Monitoring and logging are essential for observability and debugging.
- Canary deployments can help reduce risk when deploying new models.
- Presence and click-through rate are important metrics to track.
- Define a production-ready model that is tested and validated.
- Deployment can be automated using tools like TensorFlow and Google Cloud.
- Understand the resource usage of your system to ensure scalability.
- Validate your model on real-world data to ensure it generalizes well.
- Data quality is a first-class citizen in machine learning.
- Observability is critical for understanding how your system is performing.
- Collaborate with teams to ensure data quality and system reliability.
- Use third-party APIs to simplify model deployment and maintenance.
- Train models in a pipeline that can confirm expected outputs.
- Use automated testing frameworks to ensure model accuracy.
- Deploy multiple models to handle different scenarios and improve overall performance.
- Use A/B testing to validate model performance.
- Ensure data quality by implementing data imputation, filtering, and normalization.
- Use cloud-based services like Google Cloud to streamline model deployment and maintenance.
- Monitor and log your system to ensure reliability and performance.