How Agile is Your AI Team?

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Vectice enables modeling teams to auto-document directly from Python and R Code, no matter their tools, frameworks, and AI platforms.
Data Science Process Alliance
The industry’s leading AI / Data Science Project Management training and coaching organization

Jeff Saltz

Jeff created this scorecard to help teams evaluate their process. He is a leading voice in Data Science Methodologies with 7 years of published papers and leverages his 20+ years of industry experience to help teams to take advantage of emerging technologies and data analytics to deliver innovative business solutions.
Founding Member @ DSPA
Professor of ML & Applied DS @ Syracuse University
Past Director & CTO @ JP Morgan Chase
Past CTO @ Goldman Sachs
process dimension
process component
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well defined
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Life Cycle Usage
The team has a shared mental model of the steps in a data science project.
The full life cycle is defined, including bias assessment and maintaining production models
Agile Coordination
The team rapidly provides small meaningful increments to stakeholders.
The team observes, analyses and gets stakeholder feedback on each iteration.
Management and stakeholders are engaged to help prioritize potential task and projects.
Process improvement and cross team coordination
The team has a life cycle that is integrated with their agile coordination framework.
The team uses defined metrics to measure success.
There is an effective ongoing process improvement effort.
Knowledge and model managment
Models are versioned, recreatable and shared across projects.
The team uses appropriate tools to support their work.
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DATA SCIENCE PROCESS ALLIANCE

Via training and consulting, DSPA empowers individuals and teams to apply new data science specific project management processes to improve project outcomes. Unlike other organizations – DSPA is solely focused on AI / Data Science process improvement.
More about  DSPA

About Vectice

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How Agile is Your AI Team?
Your team’s process reaches 89% of its full potential maturity and effectiveness.
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Enter to win a 1:1 workshop with Jeff Saltz

Winners will be randomly selected to participate in a 1:1 workshop with Jeff Saltz which focuses on AI team methodology and agility.
Jeff created this scorecard to help teams evaluate their AI / Data Science process. He pioneered the field of AI / Data Science process methodologies 7 years ago, and is now is a leading voice in the field of AI / Data Science project management, leveraging his academic research and his 20+ years of industry experience to help teams to deliver effective AI solutions.
"My research focuses on the project management issues relating to AI / Data Science projects, and how teams should work together to “do” data science."

Jeff Saltz

Founding Member @ DSPA
Professor of ML & Applied DS @ Syracuse University
Past Director & CTO @ JP Morgan Chase
Past CTO @ Goldman Sachs

Improve Your Score

DSPA has benchmarked the best practice

Life Cycle Usage

  • Shared mental model defined and used
  • life cycle covers quality (ex. validation, bias)
  • life cycle includes operations (ex: MLOps)
  • life cycle is understood by the extended team (ex: stakeholders)

Process improvement & coordination

  • defined project, team, & business metrics
  • “Vertically Sliced” process integration
  • Regular process reviews with tracked action iteams for process & tool improvement
  • Data Science teams have a defined process to work with other teams

Agile coordination

  • prioritized list of “questions to answer”
  • questions are “small”
  • team acknowledges ambiguities
  • each iteration has analysis & feedback

knowledge & model management

  • data & code are both versioned & recreatable
  • process expert focuses on team tool usage & opportunities (as well as team process)
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