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Introduction to MLOps
Overview of MLOps
Importance of MLOps in machine learning projects
Key components of MLOps
Challenges in deploying machine learning models
Version Control for Machine Learning
Version control systems (Git, SVN)
Managing machine learning code and models in version control
Collaborative development in machine learning projects
Automated Testing for ML Models
Unit testing for machine learning code
Integration testing for machine learning models
Continuous integration and continuous deployment (CI/CD) pipelines for ML
Containerization and Orchestration
Docker and containerization for ML environments
Kubernetes for ML model orchestration
Managing dependencies and reproducibility in ML projects
Model Registry and Artifact Management
Managing and cataloging machine learning models
Artifact versioning and tracking
Model lineage and provenance
Model Deployment and Serving
Deployment strategies for ML models (REST APIs, serverless, etc.)
Scalability and performance considerations
A/B testing and canary releases for models
Continuous Monitoring and Feedback Loops
Monitoring model performance in production
Setting up alerts and triggers for model issues
Feedback loops for model retraining and updates
Security and Compliance in MLOps
Data security and privacy in ML projects
Compliance with regulations (GDPR, HIPAA, etc.)
Secure model deployment and access control
Infrastructure and Resource Management
Provisioning and managing resources for ML projects
Cost optimization and resource allocation
Managing GPU resources for deep learning
Model Explainability and Interpretability
Interpreting machine learning models
Explaining model predictions
Ethical considerations in model deployment
Governance and Documentation
Documentation best practices for ML projects
Model cataloging and documentation
Versioning and archiving of models and data
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