Workflow Element Store

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