Machine Learning in Production

  1. Model Development for Production
    1. Development Environment Setup
      1. Environment Isolation
        1. Virtual Environments
          1. Containerized Development
            1. Cloud Development Environments
            2. Development Tools and IDEs
              1. Jupyter Notebooks
                1. Integrated Development Environments
                  1. Code Quality Tools
                2. Reproducible Model Training
                  1. Experiment Tracking
                    1. Logging Parameters
                      1. Logging Metrics
                        1. Logging Artifacts
                          1. Experiment Comparison
                            1. Experiment Organization
                            2. Code Versioning with Git
                              1. Branching Strategies
                                1. Tagging and Releases
                                  1. Code Review Processes
                                  2. Environment and Dependency Management
                                    1. Dependency Specification
                                      1. Environment Configuration
                                        1. Containerization for Reproducibility
                                        2. Random Seed Management
                                          1. Deterministic Training
                                            1. Reproducible Results
                                          2. Model Training at Scale
                                            1. Distributed Training
                                              1. Data Parallelism
                                                1. Model Parallelism
                                                  1. Distributed Training Frameworks
                                                  2. Resource Management
                                                    1. GPU Utilization
                                                      1. Memory Management
                                                        1. Compute Optimization
                                                        2. Training Optimization
                                                          1. Hyperparameter Tuning
                                                            1. Early Stopping
                                                              1. Learning Rate Scheduling
                                                            2. Model and Artifact Management
                                                              1. Model Registries
                                                                1. Model Registration Process
                                                                  1. Model Metadata Management
                                                                    1. Model Discovery
                                                                    2. Model Versioning
                                                                      1. Versioning Strategies
                                                                        1. Rollback and Promotion
                                                                          1. Model Lineage Tracking
                                                                          2. Storing Model Artifacts
                                                                            1. Storage Solutions
                                                                              1. Artifact Metadata
                                                                                1. Artifact Compression
                                                                                2. Model Documentation
                                                                                  1. Model Cards
                                                                                    1. Performance Reports
                                                                                      1. Usage Guidelines
                                                                                    2. Automated Training Pipelines
                                                                                      1. Pipeline Design Principles
                                                                                        1. Modularity
                                                                                          1. Reusability
                                                                                            1. Maintainability
                                                                                            2. Scripting the Training Process
                                                                                              1. Modular Training Scripts
                                                                                                1. Parameterization
                                                                                                  1. Error Handling
                                                                                                  2. Orchestrating Training Workflows
                                                                                                    1. Workflow Scheduling
                                                                                                      1. Dependency Management
                                                                                                        1. Parallel and Distributed Training
                                                                                                        2. Pipeline Testing
                                                                                                          1. Unit Testing for ML Code
                                                                                                            1. Integration Testing
                                                                                                              1. End-to-end Testing
                                                                                                            2. Model Validation and Testing
                                                                                                              1. Cross-validation Strategies
                                                                                                                1. K-fold Cross-validation
                                                                                                                  1. Time Series Cross-validation
                                                                                                                    1. Stratified Sampling
                                                                                                                    2. Model Performance Evaluation
                                                                                                                      1. Holdout Testing
                                                                                                                        1. A/B Testing Framework
                                                                                                                          1. Statistical Significance Testing
                                                                                                                          2. Model Robustness Testing
                                                                                                                            1. Adversarial Testing
                                                                                                                              1. Stress Testing
                                                                                                                                1. Edge Case Testing

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                                                                                                                            3. Data Engineering for Production

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                                                                                                                            5. Model Deployment