📋TDSP

NEW

Also known as: Team Data Science Process

An iterative methodology for data science projects emphasizing collaboration, reproducibility, and DevOps practices across the lifecycle.

DESCRIPTION

TDSP (Team Data Science Process) is a structured, iterative methodology for organizing end‑to‑end data science work from business understanding to deployment and monitoring. It promotes standardized artifacts, version control, experiment tracking, and reproducible workflows. TDSP integrates DevOps concepts such as CI/CD for ML, environment management, and automated testing. It encourages cross‑functional collaboration between data scientists, engineers, and product stakeholders. Organizations use TDSP to reduce project risk, increase delivery predictability, and align with governance requirements.

ALTERNATIVE DEFINITIONS

Microsoft Learn

The Team Data Science Process (TDSP) is an agile, iterative data science methodology to improve collaboration and efficiency for building intelligent applications.

Highlights agility and collaboration for building intelligent applications.

Source

Azure (Microsoft) TDSP

TDSP is a lifecycle process that provides a systematic approach to conducting data science projects.

Emphasizes lifecycle structure and systematic project execution.

Source

RELATED TERMS

MLOps Agile DevOps Experiment Tracking Version Control Model Deployment Data Science Lifecycle Model Governance

COMPETING TERMS

CRISP-DM OSEMN

KNOWLEDGE AREAS

Data Science MLOps Data Management Collaboration

TARGET AUDIENCE

Data Scientist MLOps Engineer Data Engineer Data Strategy Consultant Data Management Consultant AI Product Manager Chief Data Officer

EXTERNAL SOURCES

• Microsoft Learn - Official TDSP overview guidance
• Azure (Microsoft) TDSP - Methodology assets and templates
• Microsoft Learn - TDSP lifecycle stages

TOP QUESTIONS

What is the main goal of TDSP?

TDSP aims to standardize data science project workflows for reproducibility, collaboration, and efficient delivery, aligning teams across the lifecycle from problem framing to deployment and monitoring.

Category: Overview

How does TDSP differ from CRISP-DM?

TDSP adds engineering rigor and DevOps practices to CRISP-DM’s analytical stages, emphasizing code versioning, automation, and production operations alongside iterative data science steps.

Category: Comparison

Why is TDSP relevant in modern MLOps?

It integrates CI/CD, environment management, and monitoring, helping bridge model experimentation and production reliability, which are core to MLOps best practices.

Category: Relevancy

What artifacts are commonly produced in TDSP?

Teams produce project charters, data dictionaries, experiment logs, model cards, deployment runbooks, and governance documentation to ensure clarity and repeatability.

Category: Requirements

What are common mistakes when applying TDSP?

Skipping standardized artifacts, neglecting version control for data and code, and underinvesting in automated testing lead to poor reproducibility and fragile deployments.

Category: Mistakes

How does TDSP manage model lifecycle risk?

It mandates monitoring for drift, performance alerts, retraining pipelines, and clear rollback plans, reducing operational and compliance risks in production.

Category: Risk

What roles benefit most from TDSP?

Data scientists, MLOps engineers, and data engineers benefit from shared processes; product managers and CDOs gain governance visibility and delivery predictability.

Category: Audience

Where should a team start with TDSP?

Begin with the TDSP lifecycle overview, adopt the standard templates, set up version control and experiment tracking, then iterate through discovery, modeling, and deployment.

Category: Getting Started

What is a hot topic around TDSP today?

Integrating TDSP with LLMOps practices, model cards, and AI governance checklists is trending as organizations scale generative AI responsibly.

Category: Topic