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人工智能的敏捷项目管理

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Overview

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Learning outcomes

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Course content

1

Ai需求梳理

2

敏捷迭代规划

3

数据驱动决策

4

模型快速部署

5

持续学习改进

Career Path

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Key facts

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Why this course

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People also ask

Everything you need to know before you start

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60 sec
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Course access
Self-paced
Learn on your time
Certificate
Included in fee

We offer immediate access to our course materials through our open enrollment system. This means:

  • The course starts as soon as you pay the course fee, instantly
  • No waiting periods or fixed start dates
  • Instant access to all course materials upon payment
  • Flexibility to begin at your convenience

This self-paced approach allows you to begin your professional development journey immediately, fitting your learning around your existing commitments.

We offer two flexible learning paths to suit your schedule:

  • Fast Track: Complete in 1 month with 3-4 hours of study per week
  • Standard Mode: Complete in 2 months with 2-3 hours of study per week

You can progress at your own pace and access the materials 24/7.

There are no formal entry requirements for this course. You just need:

  • A good command of English language
  • Access to a computer/laptop with internet
  • Basic computer skills
  • Dedication to complete the course
Ready when you are
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Self-paced · Certificate included · 24/7 access · 60-second start.
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Assessment is done through:

  • Multiple-choice questions at the end of each unit
  • You need to score at least 60% to pass each unit
  • You can retake quizzes if needed
  • All assessments are online

Upon successful completion, you will receive:

  • A digital certificate from London School of Business and Administration
  • Option to request a physical certificate
  • Transcript of completed units
  • Certification is included in the course fee
Open enrolment · Start today

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Why people choose us for their career

Trusted by professionals worldwide

Verified outcomes from learners who finished the course and put it to work.

4.5
Based on 4 learner reviews · 4 countries
98%
Would recommend
100%
Verified learners
2026
Cohort active
Completed from United Kingdom
ST
Sarah Thompson
GB · Course completed

Honestly, I wasn’t sure how Agile could fit with AI, but this course proved it works like a charm. The practical examples, like using Kanban boards for data‑labeling sprints, helped me organise my team’s workflow at a small AI consultancy. I walked away with a solid grasp of backlog grooming for model‑training tasks and even picked up a few handy Power‑BI dashboards to track sprint velocity. The PDFs and video tutorials were spot‑on and easy to follow. I’m pretty chuffed with what I’ve learned and can already see the impact on our project delivery.

MC
Michael Carter
US · Course completed

The "人工智能的敏捷项目管理" course precisely matched my professional development plan. The modules on sprint planning for AI model training gave me a concrete framework to integrate data‑engineering tasks into two‑week cycles. I was able to apply the taught JIRA workflow to my current project at a fintech startup, reducing our iteration time by 20%. The course materials—especially the case studies on real‑world AI deployments—were up‑to‑date and directly relevant. Overall, the instructor’s clear explanations and the hands‑on labs made the learning experience both efficient and highly satisfying.

AP
Ananya Patel
IN · Course completed

Wow! This course blew my expectations out of the water! The blend of Agile principles with AI project nuances gave me the confidence to lead my company’s first AI‑driven product launch. I especially loved the hands‑on lab where we built a sprint backlog for a computer‑vision model, and the risk‑assessment worksheet that taught me how to anticipate data‑drift issues. The course content was fresh, packed with real‑world examples from leading tech firms, and the instructor’s enthusiasm kept me engaged throughout. I’m now using the learned sprint retrospectives to continuously improve our AI pipelines—truly a game‑changer.

ZD
Zanele Dlamini
ZA · Course completed

The "人工智能的敏捷项目管理" program offered a thorough, detail‑rich exploration of how Agile can be tailored for AI initiatives. Each chapter broke down complex topics—such as iterative model validation and continuous integration of training data—into digestible steps. I applied the taught technique of defining Definition of Done for AI features, which helped my team at a health‑tech startup deliver a predictive analytics module on schedule. The supplementary reading list, especially the latest research on AI governance, added depth to the learning experience. Overall, the course was well‑structured and highly applicable to my daily work.





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Recently updated!

May 2026