Workflow Element Store

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