Workflow Element Store

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