Workflow Element Store

  1. Data Pre-existing
  2. Data Collaboration and Partnerships
  3. Public Datasets
  4. APIs and Data Feeds
  5. Crowdsourcing
  6. Unstructured data (Audio)
  7. Mobile Applications or IoT Applications
  8. Structured Data (Tabular)
  9. Data Generation
  10. Data Logging
  11. Surveys and Questionnaires
  12. Unstructured data (Images / Videos)
  13. WebScraping
  1. GCS
  2. Oracle DB
  3. Azure blob storage
  4. PostgreSQL
  5. MySQL
  6. RDBMS
  7. Informatica
  8. MS SQL server
  9. NoSQL DB
  10. GCP BigQuery
  11. AWS Redshift
  12. S3
  13. Azure Data Warehouse
  1. Binning
  2. Dealing with Outliers
  3. Handling Missing Data
  4. Feature Selection
  5. Logarithmic Transform
  6. Textual Feature Extraction
  7. Dimensionality Reduction
  8. Time-Based Features
  9. Handling Imbalanced Classes
  10. AutoEDA libraries
  11. Auto-Preprocessing libraries
  12. Domain-Specific Feature Engineering
  13. Data Scaling and Normalization
  14. Handling Time-Series Data
  15. Polynomial Features
  16. Interaction Features
  17. Handling Noisy Data
  18. Handling Categorical Data
  19. Feature Extraction from Images
  20. Encoding Categorical Variables
  21. Dimensionality Reduction
  22. Data Scaling and Normalization
  1. Supervised Learning-multiclass classification
  2. Supervised Learning-Regression
  3. Ensemble Techniques
  4. Time Series Anaysis
  5. Train-Test Split
  6. Blackbox Techniques
  7. Data Partitioning
  8. Supervised Learning-binary classification
  9. Forecasting
  10. Unsupervised Learning
  1. Data Augmentation
  2. Train-Test Split
  3. Cross-Validation
  4. Transfer Learning
  5. Weight Initialization
  6. Batch Normalization
  7. Learning Rate Scheduling
  8. Data Partition-sequential
  9. Regularization
  10. Batch Size Selection
  11. Regular Monitoring and Logging
  12. Ensemble Methods
  13. Early Stopping
  14. Gradient Clipping
  15. Hyperparameter Tuning
  1. Cross-Validation
  2. Evaluation Metrics
  3. Performance Visualization
  4. Data Partitioning
  5. Model Interpretability
  6. External Validation
  7. Train-Test Split
  8. Hyperparameter Tuning
  9. Model Comparison
  10. Regularization Techniques
  1. Feedback Collection
  2. Error Analysis
  3. Containerization
  4. Streamlit
  5. A/B Testing
  6. Documentation and Reporting
  7. Prediction Logging
  8. Edge Deployment
  9. Model Registry
  10. Serverless Computing
  11. Bias and Fairness Assessment
  12. Monitoring and Logging
  13. Security Considerations
  14. Cloud Deployment
  15. Concept Drift Detection
  16. Performance Metrics
  17. Model Drift
  18. Web APIs - Flask, FastAPI, etc.
  19. Model Serialization
  20. Model Health Monitoring
  21. Model Retraining and Updating
  22. Continuous Integration and Deployment (CI/CD)
  23. Model Versioning
  24. Data Drift Monitoring
  25. Model Monitoring and Maintenance
  26. Documentation and API Documentation
  27. Alerting and Notification
  1. End User Machine
  2. Mobile
ML Workflow Beginner - Architecture
  • Element belongs to model
  • Element not belongs to model
Feature Store

Feature Store
(Online / Offline)

Data Sources

Data Sources

Data Warehouse

Data Warehouse/ Data Lake

Data Pre Processing & Feature Engineering

EDA, Data Pre Processing & Feature Engineering

Model Selection

Model Selection

Model Training & Hyper Parameter Tuning

Model Training & Hyper Parameter Tuning

Model Evaluation

Model Evaluation

Model Deployment

Model Deployment

End User Device

End User Device

Model Registry

Model Registry