1. MLOps - what it is and why you can't do without it

• When things get serious with machine learning projects
• Domain knowledge and challenges
• The MLOps cycle at a glance
• MLOps is more than DevOps
• The MLOps maturity levels

2. data versioning and experiment tracking

• Basics and advantages of code and data versioning
• Introduction to DVC
• Exercise: Data versioning with DVC
• Exercise: Experiment Tracking with DVC

3. data pipeline orchestration

• Basics and advantages of data pipelines
• Introduction to Dagster
• Exercise: Asset jobs with Dagster
• Exercise: Op jobs with Dagster

4. experiment tracking

• Parameters, metrics and artifacts
• Basics and advantages of experiment tracking
• Experiment tracking with MLflow
• Exercise: Experiment tracking with MLflow
• Exercise: Model management with MLflow

5. CI/CD for machine learning

• Introduction to CI/CD, differentiation of CI/CD for code
• What can we test?
• Variants of CI/CD for ML products
• Showcase: Github Actions and CML

6. deployment and serving

• Basics of machine learning deployment
• Differentiation between batch inference and live inference
• Data preprocessing in deployment
• Introduction to Open Neural Network Exchange (ONNX)
• Exercise: FastAPI and ONNX

7. monitoring

• Monitoring of ML models
• Data, metrics, KPIs
• Application metrics
• Showcase: Monitoring with evidently.ai

8 MLOps in the cloud

• When are cloud solutions recommended?
• Classification Amazon Sagemaker, Azure ML Studio and Google Vertex AI
• Showcase: Model training with Azure ML Studio

9. machine learning platforms

• How and when do I scale the development of my ML teams?
• What is a feature store?

10. excursus: LLMOps

• What distinguishes LLMOps from MLOps?
• Showcase: companyGPT