Workflow Element Store

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