Workflow Element Store

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