Workflow Element Store

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