Workflow Element Store

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