Workflow Element Store

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