Workflow Element Store

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