Workflow Element Store

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