Workflow Element Store

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