Workflow Element Store

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