Release Notes
Product Introduction
Purchase Guide
Getting Started
Buddy
- Buddy - Data Analytics Scenarios
- Data Engineering Agent
- Buddy Overview
- Data Governance Agent
- Buddy Data Security
- Overview of Buddy Agent Basic Capabilities
Platform Management
- Workspace Management
- Compute Resource Management
- Data Source Management Overview
Data Integration
- Supported Data Ingestion Solutions
- Supported Data Sources and Read Capabilities
- Data Source Management
- List of Data Sources
- Offline Data Synchronization
- Real-Time Data Synchronization
- File Upload
Data Engineering
- Studio
- Workflow
- CI/CD
Data Science
- Data Science Overview
- Model Experiments
- Feature Management
- Model Management
- Model Service
Data Analysis
- Data Exploration Overview
Data Governance
- Data Catalog Overview
- Data Discovery Overview
- Data Security Overview
- Data Quality Overview
- Semantic Model Overview
- Governance Tag Overview
Data Applications
- Agent Development
- MCP
- Model List
API Documentation
- History
- Introduction
- API Category
- Making API Requests
- Console APIs
- Workspace APIs
- Computational Resource APIs
- Development Studio APIs
- Workflow APIs
- Catalog APIs
- Data Types
- Error Codes
FAQs
Agreements
Contact Us
Glossary

Data Science Overview

Baixar

Modo Foco

Tamanho da Fonte

Última atualização: 2026-10-09 16:13:59

Traduzido por IA

Data Science is a module in DataBuddy designed for data scientists and machine learning engineers to support end-to-end machine learning development, covering the complete workflow from experiment tracking, feature engineering, and model registration to model serving and deployment. Built on the open-source MLflow platform, this module connects machine learning artifacts with the unified governance framework of DataBuddy Catalog, enabling unified management of machine learning assets and data assets from the same source.
Tip
For more MLflow features and usage, see the official MLflow documentation.
Module Positioning
The Data Science module is built on the MLOps philosophy, providing standardized, automated, and continuously improvable process management for the full lifecycle of machine learning models. It helps enterprises produce models in a stable, scalable, and high-quality manner to empower business. Its core principles are as follows:
Drive AI project development guided by business objectives.
Achieve AI development with a data-centric approach.
Drive the full lifecycle with a modular platform: data exploration, feature engineering, model training, and online serving.
Achieve continuous training, continuous integration, and continuous delivery through automated processes.
Feature Overview
The DataBuddy Data Science module builds four core functional modules: experiment management, feature management, model management, and model serving. It integrates with peripheral products such as the development workbench, workflows, data quality, and computing resources to deliver end-to-end capabilities across the full lifecycle of experiment tracking, feature reuse, model version management, and online inference services, establishing a closed-loop MLOps system.
The development process typically starts with loading and cleaning data in the development workbench, and then registering the data with primary keys and timestamps as a feature table for reuse. Next, create experiments to record parameters, metrics, and model artifacts through custom training or AutoML, and compare multiple runs to select the best model. Register the desired model to Catalog for unified management, and finally deploy it as an online inference service to provide capabilities externally.
Core Modules
Module
Core Feature
Detailed documentation
Model Experiment
The model experiment panel tracks training parameters, metrics, and artifacts; it supports three types of no-code experiments: custom model experiments, agent experiments, and AutoML experiments (classification / regression / time series forecasting).
Model Experiment
Feature Management
In Development Workbench, for each feature table, you can use the Feature Engineering SDK to perform operations such as creation, writing, reading, searching, synchronization, and consumption, and view and manage features on the Feature Management page to achieve unified feature management and consumption.
Feature Management
Model Management
You can register models in experiments by calling relevant MLflow functions, or perform visual model registration in experiment management. In the management interface, you can view key information of registered models and their associations with experiments/runs, services, and other entities.
Model Management
Model Service
Deploy registered models as online inference APIs, supporting features such as version switching, traffic distribution, and resource monitoring.
Model Serving
Peripheral Modules
Module
Collaboration with data science
Development Workbench
Provides a development environment where users can edit, debug, and run code in Development Workbench, and call MLflow and Feature Engineering APIs to perform operations such as feature table creation, modification, query, and deletion, model training, and model registration.
Catalog
Unified storage and governance foundation for models and feature tables, providing permissions, tags, and lineage
Workflow
Provides a workspace for automated workflows. After debugging code in Development Workbench, users can submit it to workflows to configure periodic scheduling, enabling automated and periodic model production.
Data Quality
Model serving inference tables, feature tables, and training data tables can all initiate data quality jobs to view quality information such as field analysis, drift analysis, and model metrics.
Prerequisites and Important Notes
Type
Description
Development Workbench
You must enable Development Workbench before you can use data science-related features. Development Workbench is the primary workspace for AI development, where users can edit, debug, and run code, and call MLflow and Feature Engineering APIs to perform operations such as feature table creation, modification, query, and deletion, model training, and model registration.
Compute Resource Support
Compute resources of DataBuddy Development Workbench can be used for model training, experiment reporting, feature management, and model registration. For AutoML experiments, select the Machine Learning image when creating compute resources (not the default base image).
MLflow Version
Experiment management is compatible with MLflow versions greater than or equal to 3.0.0 and less than 4.0.0. The relevant SDK is automatically installed by a pre-execution script when the Kernel starts, rather than being preinstalled in the image. After connecting to the Development Workbench runtime environment, run the following command to check:
%pip list | grep mlflow
Official documentation: MLflow documentation
Offline Feature Store
Offline feature tables support only tables in Catalog. Tables with a primary key and a timestamp are automatically identified as feature tables. When registering a feature table, you must specify the primary key and timestamp key, which are then used for feature indexing.
Online Feature Store
DataBuddy currently supports only PostgreSQL as the online feature store. To prepare, follow these steps:
Go to the cloud database to create a PostgreSQL instance. The region and network must be consistent with the scheduling resource group and the engine in use.
Add a PostgreSQL data source in **Platform Management > Data Source Management** of DataBuddy, and test connectivity to the scheduling resource group.
After connecting to the remote kernel in the development workbench, verify whether PostgreSQL is connected:
import psycopg2
PG_HOST = "myhost"   # Example: "10.0.0.51"
PG_PORT = "myport"   # Example: "5432"
PG_DB   = "mydb"
PG_USER = "myuser"
PG_PWD  = "mypwd"     # If no password is set, comment out this line and the password parameter below.
try:
    conn = psycopg2.connect(
        host=PG_HOST,
        port=PG_PORT,
        dbname=PG_DB,
        user=PG_USER,
        password=PG_PWD,
        connect_timeout=3,
    )
    conn.close()
    print("PostgreSQL is reachable")
except Exception as e:
    print("PostgreSQL connection failed:", e)
Table Operation Permission
Grant permissions in **Compute Resources > Corresponding Resource > More > Permissions**, and grant permissions in DataBuddy Catalog > Corresponding Catalog / Schema / Table > Permissions.
Model Operation Permission
Grant permissions to users in DataBuddy Catalog > Corresponding Model Catalog > Permissions.
Feature Engineering Package
After connecting to the engine, run the following command to install the feature engineering package (version 0.2.3 or later required):
%pip install "wedata3-feature-engineering[mlflow3]>=0.2.3"
Run the following command to check the version:
%pip list | grep wedata3-feature-engineering
Environment Variable Settings
In DataBuddy, the MLflow / Feast service addresses, temporary credentials, and other settings are automatically injected by the pre-execution script when the Kernel starts, so users do not need to configure them manually.
References
Model Experiment
Feature Management
Model Management
Model Serving
Development Workbench Overview
Data Catalog Overview
Compute Resource Overview

Ajuda e Suporte

Esta página foi útil?

Você também pode entrar em contato com a Equipe de vendas ou Enviar um tíquete em caso de ajuda.

comentários

tencent cloud

Tencent Cloud DataBuddy

Data Science Overview

Module Positioning

Feature Overview

Core Modules

Peripheral Modules

Prerequisites and Important Notes

References

Ajuda e Suporte

Module	Core Feature	Detailed documentation
Model Experiment	The model experiment panel tracks training parameters, metrics, and artifacts; it supports three types of no-code experiments: custom model experiments, agent experiments, and AutoML experiments (classification / regression / time series forecasting).	Model Experiment
Feature Management	In Development Workbench, for each feature table, you can use the Feature Engineering SDK to perform operations such as creation, writing, reading, searching, synchronization, and consumption, and view and manage features on the Feature Management page to achieve unified feature management and consumption.	Feature Management
Model Management	You can register models in experiments by calling relevant MLflow functions, or perform visual model registration in experiment management. In the management interface, you can view key information of registered models and their associations with experiments/runs, services, and other entities.	Model Management
Model Service	Deploy registered models as online inference APIs, supporting features such as version switching, traffic distribution, and resource monitoring.	Model Serving

Module	Collaboration with data science
Development Workbench	Provides a development environment where users can edit, debug, and run code in Development Workbench, and call MLflow and Feature Engineering APIs to perform operations such as feature table creation, modification, query, and deletion, model training, and model registration.
Catalog	Unified storage and governance foundation for models and feature tables, providing permissions, tags, and lineage
Workflow	Provides a workspace for automated workflows. After debugging code in Development Workbench, users can submit it to workflows to configure periodic scheduling, enabling automated and periodic model production.
Data Quality	Model serving inference tables, feature tables, and training data tables can all initiate data quality jobs to view quality information such as field analysis, drift analysis, and model metrics.

Type	Description
Development Workbench	You must enable Development Workbench before you can use data science-related features. Development Workbench is the primary workspace for AI development, where users can edit, debug, and run code, and call MLflow and Feature Engineering APIs to perform operations such as feature table creation, modification, query, and deletion, model training, and model registration.
Compute Resource Support	Compute resources of DataBuddy Development Workbench can be used for model training, experiment reporting, feature management, and model registration. For AutoML experiments, select the Machine Learning image when creating compute resources (not the default base image).
MLflow Version	Experiment management is compatible with MLflow versions greater than or equal to 3.0.0 and less than 4.0.0. The relevant SDK is automatically installed by a pre-execution script when the Kernel starts, rather than being preinstalled in the image. After connecting to the Development Workbench runtime environment, run the following command to check: %pip list \| grep mlflow Official documentation: MLflow documentation
Offline Feature Store	Offline feature tables support only tables in Catalog. Tables with a primary key and a timestamp are automatically identified as feature tables. When registering a feature table, you must specify the primary key and timestamp key, which are then used for feature indexing.
Online Feature Store	DataBuddy currently supports only PostgreSQL as the online feature store. To prepare, follow these steps: Go to the cloud database to create a PostgreSQL instance. The region and network must be consistent with the scheduling resource group and the engine in use. Add a PostgreSQL data source in Platform Management > Data Source Management of DataBuddy, and test connectivity to the scheduling resource group. After connecting to the remote kernel in the development workbench, verify whether PostgreSQL is connected: import psycopg2 PG_HOST = "myhost" # Example: "10.0.0.51" PG_PORT = "myport" # Example: "5432" PG_DB = "mydb" PG_USER = "myuser" PG_PWD = "mypwd" # If no password is set, comment out this line and the password parameter below. try: conn = psycopg2.connect( host=PG_HOST, port=PG_PORT, dbname=PG_DB, user=PG_USER, password=PG_PWD, connect_timeout=3, ) conn.close() print("PostgreSQL is reachable") except Exception as e: print("PostgreSQL connection failed:", e)
Table Operation Permission	Grant permissions in Compute Resources > Corresponding Resource > More > Permissions, and grant permissions in DataBuddy Catalog > Corresponding Catalog / Schema / Table > Permissions.
Model Operation Permission	Grant permissions to users in DataBuddy Catalog > Corresponding Model Catalog > Permissions.
Feature Engineering Package	After connecting to the engine, run the following command to install the feature engineering package (version 0.2.3 or later required): %pip install "wedata3-feature-engineering[mlflow3]>=0.2.3" Run the following command to check the version: %pip list \| grep wedata3-feature-engineering
Environment Variable Settings	In DataBuddy, the MLflow / Feast service addresses, temporary credentials, and other settings are automatically injected by the pre-execution script when the Kernel starts, so users do not need to configure them manually.