We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Dr. Paul Elvers: Getting Started with MLOps: Best Practices for Production-Ready ML Systems | PyData
Learn the best practices for production-ready machine learning systems with Dr. Paul Elvers' talk on MLOps, covering lifecycle management, architecture, data, analytics, and essential tooling for successful collaboration and automation.
- MLOps is a field that combines machine learning and software engineering to manage the lifecycle of ML models.
- ML system involves data storage, data processing, model training, model serving, model evaluation, and model monitoring.
- Four dimensions of MLOps: problem, architecture, data, and analytics.
- Important components of a minimal ML system: data storage, data processing, model training, model serving, model evaluation, and model monitoring.
- Model training involves data exploration, model development, model evaluation, and model tuning.
- Model serving involves deploying the trained model to a production environment.
- Model evaluation involves tracking the performance of the deployed model.
- Model monitoring involves tracking the performance of the deployed model over time.
- Iterative approach to MLOps: build, iterate, and refine.
- Important aspects of MLOps: experimentation, automation, version control, and collaboration.
- Tooling landscape for MLOps is complex and fragmented.
- Popular tools for MLOps: Git, Jenkins, Docker, Kubernetes, Airflow, MLflow.
- Open-source options for MLOps: Kubeflow, Datalift.
- Automation and orchestration are important aspects of MLOps.
- Business KPIs are important for MLOps: customer engagement, revenue growth, user satisfaction.
- Data quality is important for MLOps: data preprocessing, data cleaning, data augmentation.
- Model interpretability is important for MLOps: model explainability, feature importance.
- Collaboration is important for MLOps: data scientists, engineers, and businesses working together.