Workflow Element Store

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