Workflow Element Store

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

Feature Store
(Online / Offline)

Data Sources

Data Warehouse/ Data Lake

EDA, Data Pre Processing & Feature Engineering

Model Selection

Model Training & Hyper Parameter Tuning

Model Evaluation

Model Deployment

End User Device

Model Registry