Workflow Element Store

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