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Workflow Element Store
- Feedback Data
- APIs and Data Feeds
- WebScraping
- Data Collaboration and Partnerships
- Surveys and Questionnaires
- Public Datasets
- Flat files
- Data bases - NoSQL
- Experiments (DoE)
- Mobile Applications or IoT Applications
- Data Bases - SQL
- Azure Synapse
- s3
- PostgreSQL
- MongoDB
- AWS Redshift
- Azure ADF
- MS SQL server
- GCP Dataflow
- Apache Kafka
- AWS Kinesis
- GCP BigQuery
- Azure Streaming Analytics
- GCP Data Fusion
- AWS RDS
- MySQL
- Azure blob storage
- AWS Glue
- GCS
- Oracle DB
- ETL/ELT pipeline
- RDBMS
- Dealing with Outliers
- Handling Categorical Data
- Domain-Specific Feature Engineering
- Auto-Preprocessing libraries
- Handling Imbalanced Classes
- Augmentation
- Annotation
- Textual Feature Extraction
- Interaction Features
- Dimensionality Reduction
- Handling Missing Data
- Data Scaling and Normalization
- Data Transformations
- Handling Noisy Data
- Feature Extraction from Images
- Handling Time-Series Data
- Feature Selection
- AutoEDA libraries
- Binning / Discretization
- Polynomial Features
- Time-Based Features
- Data Partitioning - Train, Validation, & Test
- Cross-Validation
- Multiclass Classification Techniques
- Association Rules
- Natural Language Processing
- Binary Classification Techniques
- Regression Analysis
- AutoML
- Batch Size Selection
- Data Augmentation
- Batch Normalization
- Evaluation Metrics
- Network Analytics/ GeoSpatial Analytics
- Transfer Learning
- Recommendation Engine
- GridSearchCV, RandomisedSearchCV, BayesianSearchCV
- External Validation
- Word Embeddings
- Regular Monitoring and Logging
- Performance Visualization
- Cross-Validation
- Early Stopping
- Blackbox - Neural Network Models
- Transfer Learning
- Regularization Techniques
- Model Comparison
- Clustering
- Reinforcement Learning
- Forecasting Techniques
- Weight Initialization
- Hyperparameter Tuning
- Learning Rate Scheduling
- Ensemble Techniques
- Model Interpretability
- Regularization
- model registry
- code repository
- Databases
- Datawarehouse
- Data Preprocessing pipeline models
- Model Versioning
- Model Drift
- Data Drift Monitoring
- Edge Deployment
- Streamlit
- Cloud Deployment
- Prediction Logging
- Bias and Fairness Assessment
- Model Serialization
- Feedback Collection
- Containerization
- Flask
- FastAPI
- Concept Drift Detection
- Alerting and Notification
- Performance Metrics
- Serverless Computing
- Model Health Monitoring
Training Pipeline
Data Collection
API Stream
Web crawler
API Stream
Web crawler
Selenium
Data Ingestion
Data Landing Zone
Store Data from all the Sources
Data Cleaning / Preprocessing
Derived & Base features
Data Training & Modelling
Inference Pipeline
Input Data for Forecasting
Input Data
Cleaned & Processed Data