Workflow Element Store

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