Workflow Element Store

  1. Mobile Applications or IoT Applications
  2. Surveys and Questionnaires
  3. WebScraping
  4. Data Collaboration and Partnerships
  5. Data Pre-existing
  6. Unstructured data (Images / Videos)
  7. APIs and Data Feeds
  8. Structured Data (Tabular)
  9. Unstructured data (Audio)
  10. Crowdsourcing
  11. Data Logging
  12. Public Datasets
  13. Data Generation
  1. RDBMS
  2. Azure Data Warehouse
  3. Informatica
  4. MySQL
  5. MS SQL server
  6. Azure blob storage
  7. GCS
  8. PostgreSQL
  9. NoSQL DB
  10. S3
  11. AWS Redshift
  12. GCP BigQuery
  13. Oracle DB
  1. Interaction Features
  2. Feature Selection
  3. Logarithmic Transform
  4. Dealing with Outliers
  5. Handling Imbalanced Classes
  6. Handling Time-Series Data
  7. Domain-Specific Feature Engineering
  8. Binning
  9. Encoding Categorical Variables
  10. Handling Missing Data
  11. Handling Categorical Data
  12. Polynomial Features
  13. Textual Feature Extraction
  14. Handling Noisy Data
  15. Dimensionality Reduction
  16. Data Scaling and Normalization
  17. Data Scaling and Normalization
  18. Auto-Preprocessing libraries
  19. Time-Based Features
  20. Dimensionality Reduction
  21. AutoEDA libraries
  22. Feature Extraction from Images
  1. Supervised Learning-multiclass classification
  2. Train-Test Split
  3. Supervised Learning-Regression
  4. Ensemble Techniques
  5. Unsupervised Learning
  6. Supervised Learning-binary classification
  7. Time Series Anaysis
  8. Blackbox Techniques
  9. Data Partitioning
  10. Forecasting
  1. Hyperparameter Tuning
  2. Batch Normalization
  3. Batch Size Selection
  4. Data Augmentation
  5. Ensemble Methods
  6. Early Stopping
  7. Learning Rate Scheduling
  8. Transfer Learning
  9. Data Partition-sequential
  10. Gradient Clipping
  11. Regularization
  12. Train-Test Split
  13. Weight Initialization
  14. Regular Monitoring and Logging
  15. Cross-Validation
  1. Regularization Techniques
  2. Cross-Validation
  3. Data Partitioning
  4. Hyperparameter Tuning
  5. Train-Test Split
  6. Model Interpretability
  7. Evaluation Metrics
  8. External Validation
  9. Performance Visualization
  10. Model Comparison
  1. Model Retraining and Updating
  2. Security Considerations
  3. Performance Metrics
  4. Containerization
  5. Model Registry
  6. Data Drift Monitoring
  7. Model Serialization
  8. Documentation and Reporting
  9. Streamlit
  10. Model Drift
  11. Concept Drift Detection
  12. Model Health Monitoring
  13. A/B Testing
  14. Bias and Fairness Assessment
  15. Feedback Collection
  16. Cloud Deployment
  17. Prediction Logging
  18. Monitoring and Logging
  19. Continuous Integration and Deployment (CI/CD)
  20. Web APIs - Flask, FastAPI, etc.
  21. Model Monitoring and Maintenance
  22. Alerting and Notification
  23. Documentation and API Documentation
  24. Edge Deployment
  25. Serverless Computing
  26. Model Versioning
  27. Error Analysis
  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