Workflow Element Store

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