Workflow Element Store

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