Workflow Element Store

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