Workflow Element Store

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

Data Sources

Data Warehouse/ Data Lake

EDA, Data Pre Processing & Feature Engineering

Model Selection

Model Training & Hyper Parameter Tuning

Model Evaluation

Model Deployment

End User Device

Model Registry