Workflow Element Store

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

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