01.01-getting started with mlops
01.02-scope and prerequisites
02.01-machine learning life cycle
02.02-unique challenges with ml
02.03-what is devops
02.04-what is mlops
02.05-principles of mlops
02.06-when to start mlops
03.01-selecting ml projects
03.02-creating requirements
03.03-designing the ml workflow
03.04-assembling the team
03.05-choosing tools and technologies
04.01-managed data pipelines
04.02-automated data validation
04.03-managed feature stores
04.04-data versioning
04.05-data governance
04.06-tools and technologies for data processing
05.01-managed training pipelines
05.02-creating data labels
05.03-experiment tracking
05.04-automl
05.05-tools and technologies for training
06.01-model versioning
06.02-model registry
06.03-benchmarking models
06.04-model life cycle management
06.05-tools and technologies for model management
07.01-solution integration pipelines
07.02-notebook to software
07.03-solution integration patterns
07.04-best practices for solution integration
08.01-continuing on with mlops
