Workflow Element Store

  1. Data Pre-existing
  2. APIs and Data Feeds
  3. Data Generation
  4. Mobile Applications or IoT Applications
  5. Unstructured data (Audio)
  6. Unstructured data (Images / Videos)
  7. Surveys and Questionnaires
  8. WebScraping
  9. Data Logging
  10. Public Datasets
  11. Data Collaboration and Partnerships
  12. Structured Data (Tabular)
  13. Crowdsourcing
  1. Azure Data Warehouse
  2. MS SQL server
  3. Oracle DB
  4. Informatica
  5. Azure blob storage
  6. NoSQL DB
  7. RDBMS
  8. GCP BigQuery
  9. GCS
  10. PostgreSQL
  11. S3
  12. AWS Redshift
  13. MySQL
  1. Feature Extraction from Images
  2. AutoEDA libraries
  3. Handling Imbalanced Classes
  4. Data Scaling and Normalization
  5. Dimensionality Reduction
  6. Textual Feature Extraction
  7. Encoding Categorical Variables
  8. Binning
  9. Handling Time-Series Data
  10. Auto-Preprocessing libraries
  11. Data Scaling and Normalization
  12. Feature Selection
  13. Domain-Specific Feature Engineering
  14. Polynomial Features
  15. Logarithmic Transform
  16. Handling Missing Data
  17. Dealing with Outliers
  18. Handling Categorical Data
  19. Interaction Features
  20. Time-Based Features
  21. Dimensionality Reduction
  22. Handling Noisy Data
  1. Ensemble Techniques
  2. Time Series Anaysis
  3. Blackbox Techniques
  4. Unsupervised Learning
  5. Supervised Learning-binary classification
  6. Supervised Learning-Regression
  7. Data Partitioning
  8. Forecasting
  9. Train-Test Split
  10. Supervised Learning-multiclass classification
  1. Cross-Validation
  2. Weight Initialization
  3. Learning Rate Scheduling
  4. Hyperparameter Tuning
  5. Regularization
  6. Ensemble Methods
  7. Regular Monitoring and Logging
  8. Gradient Clipping
  9. Early Stopping
  10. Data Augmentation
  11. Data Partition-sequential
  12. Batch Size Selection
  13. Batch Normalization
  14. Train-Test Split
  15. Transfer Learning
  1. Train-Test Split
  2. Model Comparison
  3. Data Partitioning
  4. Model Interpretability
  5. Cross-Validation
  6. External Validation
  7. Evaluation Metrics
  8. Hyperparameter Tuning
  9. Performance Visualization
  10. Regularization Techniques
  1. Documentation and API Documentation
  2. Model Registry
  3. Model Monitoring and Maintenance
  4. Feedback Collection
  5. Model Drift
  6. Continuous Integration and Deployment (CI/CD)
  7. Model Retraining and Updating
  8. Documentation and Reporting
  9. Model Health Monitoring
  10. Streamlit
  11. Performance Metrics
  12. Data Drift Monitoring
  13. Web APIs - Flask, FastAPI, etc.
  14. Model Versioning
  15. Prediction Logging
  16. Alerting and Notification
  17. Security Considerations
  18. Monitoring and Logging
  19. Edge Deployment
  20. Model Serialization
  21. Serverless Computing
  22. Cloud Deployment
  23. Error Analysis
  24. Concept Drift Detection
  25. Containerization
  26. A/B Testing
  27. Bias and Fairness Assessment
  1. End User Machine
  2. Mobile
ML Workflow Beginner - Architecture
  • Element belongs to model
  • Element not belongs to model

Feature Store
(Online / Offline)

Data Sources

Data Warehouse/ Data Lake

EDA, Data Pre Processing & Feature Engineering

Model Selection

Model Training & Hyper Parameter Tuning

Model Evaluation

Model Deployment

End User Device

Model Registry