Workflow Element Store

  1. Mobile Applications or IoT Applications
  2. APIs and Data Feeds
  3. Public Datasets
  4. Data Collaboration and Partnerships
  5. Structured Data (Tabular)
  6. Data Logging
  7. WebScraping
  8. Surveys and Questionnaires
  9. Data Pre-existing
  10. Data Generation
  11. Crowdsourcing
  12. Unstructured data (Audio)
  13. Unstructured data (Images / Videos)
  1. Azure Data Warehouse
  2. S3
  3. GCS
  4. GCP BigQuery
  5. AWS Redshift
  6. Oracle DB
  7. Informatica
  8. NoSQL DB
  9. PostgreSQL
  10. Azure blob storage
  11. RDBMS
  12. MS SQL server
  13. MySQL
  1. Domain-Specific Feature Engineering
  2. Polynomial Features
  3. Encoding Categorical Variables
  4. Binning
  5. Dimensionality Reduction
  6. Data Scaling and Normalization
  7. AutoEDA libraries
  8. Handling Imbalanced Classes
  9. Textual Feature Extraction
  10. Dealing with Outliers
  11. Feature Extraction from Images
  12. Handling Noisy Data
  13. Handling Categorical Data
  14. Data Scaling and Normalization
  15. Feature Selection
  16. Time-Based Features
  17. Dimensionality Reduction
  18. Handling Missing Data
  19. Interaction Features
  20. Auto-Preprocessing libraries
  21. Handling Time-Series Data
  22. Logarithmic Transform
  1. Blackbox Techniques
  2. Supervised Learning-binary classification
  3. Train-Test Split
  4. Forecasting
  5. Supervised Learning-Regression
  6. Data Partitioning
  7. Time Series Anaysis
  8. Unsupervised Learning
  9. Ensemble Techniques
  10. Supervised Learning-multiclass classification
  1. Batch Normalization
  2. Learning Rate Scheduling
  3. Regularization
  4. Train-Test Split
  5. Gradient Clipping
  6. Hyperparameter Tuning
  7. Batch Size Selection
  8. Data Augmentation
  9. Weight Initialization
  10. Ensemble Methods
  11. Early Stopping
  12. Cross-Validation
  13. Data Partition-sequential
  14. Transfer Learning
  15. Regular Monitoring and Logging
  1. Model Interpretability
  2. External Validation
  3. Cross-Validation
  4. Train-Test Split
  5. Regularization Techniques
  6. Model Comparison
  7. Hyperparameter Tuning
  8. Data Partitioning
  9. Evaluation Metrics
  10. Performance Visualization
  1. Security Considerations
  2. Containerization
  3. Model Serialization
  4. Web APIs - Flask, FastAPI, etc.
  5. Streamlit
  6. Serverless Computing
  7. Documentation and API Documentation
  8. Model Monitoring and Maintenance
  9. Model Versioning
  10. Continuous Integration and Deployment (CI/CD)
  11. Cloud Deployment
  12. Model Registry
  13. Edge Deployment
  14. Concept Drift Detection
  15. Model Retraining and Updating
  16. Bias and Fairness Assessment
  17. Alerting and Notification
  18. Error Analysis
  19. Prediction Logging
  20. Documentation and Reporting
  21. Performance Metrics
  22. Model Drift
  23. A/B Testing
  24. Data Drift Monitoring
  25. Feedback Collection
  26. Monitoring and Logging
  27. Model Health Monitoring
  1. Mobile
  2. End User Machine
ML Workflow Beginner - Architecture
  • Element belongs to model
  • Element not belongs to model
Feature Store

Feature Store
(Online / Offline)

Data Sources

Data Sources

Data Warehouse

Data Warehouse/ Data Lake

Data Pre Processing & Feature Engineering

EDA, Data Pre Processing & Feature Engineering

Model Selection

Model Selection

Model Training & Hyper Parameter Tuning

Model Training & Hyper Parameter Tuning

Model Evaluation

Model Evaluation

Model Deployment

Model Deployment

End User Device

End User Device

Model Registry

Model Registry