Workflow Element Store

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