Workflow Element Store

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