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1. Harnessing the Power of
Computer Vision and Deep Learning
AI & ML Project Management
How to effectively manage AI/ML projects
Ana Stojkovic Knezevic
PM @Protech doo

2. Introduction to AI Project Management
1
Brief overview of AI project
management complexities and the
necessity for specialized frameworks.
•AI project management integrates
specialized frameworks to address
unique challenges.
•Necessity for flexibility and
adaptability in managing AI
projects.
•Importance of aligning AI initiatives
with organizational goals.
•Role of methodologies like Data
Driven Scrum (DDS) in enhancing
project efficiency.

3. Understanding AI Technologies
2
Importance of grasping AI capabilities and
limitations for project planning and
execution.
•Grasping AI capabilities and limitations
is crucial for effective project planning
and execution.
•Knowledge of AI technologies helps set
realistic project goals and manage
expectations.
•Understanding AI enables informed
decision-making throughout the project
lifecycle.
•Staying updated with AI trends ensures
projects align with the latest
technological standards.

4. The Role of an AI Project Manager
3
Highlighting the bridge between technical AI
aspects and strategic organizational goals.
•Acts as a bridge between technical AI
teams and strategic business objectives.
•Ensures AI projects align with and support
overall organizational goals.
•Balances technical challenges with
business requirements.
•Facilitates communication between AI
experts and business stakeholders.
•Oversees the integration of AI solutions
into business processes for maximum
impact.

5. Agile Methodologies in AI
4
Introduction to Agile and its adaptation for
AI through Data Driven Scrum (DDS).
•Introduction to Agile methodologies
and their principles.
•Overview of how Agile methodologies
are adapted for AI projects.
•Explanation of Data Driven Scrum (DDS)
as an Agile framework for AI.
•Benefits of using DDS in managing AI
projects, including flexibility and
iterative learning.
•Comparison of DDS with traditional
Scrum to highlight its adaptability to AI
project needs.

6. Data Driven Scrum (DDS) Overview
5
Key features of DDS and its advantages in
managing AI projects.
•DDS integrates variable iteration lengths to
match the exploratory demands of AI projects.
•Prioritizes data-driven decision-making to
navigate changes and uncertainties effectively.
•Encourages a culture of experimentation and
iterative learning among team members.
•Modifies traditional Scrum methodologies to
address the unique challenges of AI
development.
•Offers a comprehensive framework that
combines structure with the flexibility needed
for AI projects.

7. Implementing DDS in AI Projects
7
Step-by-step guide to applying DDS in AI
project management, including iteration
planning and execution.
•Outlines a step-by-step approach to
applying DDS in AI project management.
•Details the process of iteration planning,
emphasizing the selection of priorities
based on data insights.
•Describes the execution phase, focusing
on data analysis, AI model iteration, and
team collaboration.
•Highlights the iterative and collaborative
nature of DDS, emphasizing agile and
data-driven methodologies.

8. Key Roles in DDS
8
Description of roles within DDS: Product
Owner, Process Expert, and DDS Team
Members.
•Defines the Product Owner as the visionary
leading the project, focusing on value
delivery and prioritization.
•Introduces the Process Expert role,
responsible for facilitating the DDS process
and ensuring team efficiency.
•Details DDS Team Members, emphasizing
their cross-functional skills and collaborative
work towards project goals.
•Explains how these roles interact to drive
project success in a DDS framework,
fostering innovation and agility.

9. Challenges and Solutions in AI Project Management
9
Common AI project challenges and how
DDS addresses them, focusing on iterative
learning and adaptation.
•Identifies common AI project challenges
such as data complexity, model
inaccuracy, and team alignment issues.
•Explains how DDS promotes iterative
learning to continually refine AI models
based on new data and insights.
•Highlights the adaptation feature of
DDS, allowing teams to pivot strategies
in response to project challenges.
•Showcases the role of DDS in improving
team collaboration and alignment on
project goals and methodologies.

10. Use Case: Improving Data Annotation Processes
12
Objective: Enhance the quality and efficiency of data annotation for object detection tasks.
Approach:
1.Sprint Planning:
○Define scope: Streamline the data annotation process.
○Identify tools and methods: Use automated annotation tools and manual
validation.
2.Data Collection & Preparation:
○Gather a diverse set of images representing various object categories.
○Use both automated tools and human annotators to label images.
3.Sprint Execution:
○Validate annotations: Ensure accuracy and consistency.
○Train models on annotated data to identify potential annotation errors.
4.Review & Retrospective:
○Review model performance: Identify if annotation quality affects results.
○Plan improvements: Enhance annotation tools and provide better training for
annotators.
Outcome: Improved data annotation processes, leading to higher quality training data and
better model performance.

11. Use Case: Developing Real-Time Object Detection
Systems
11
Objective: Build a real-time object detection system for autonomous marine navigation.
Approach:
1.Sprint Planning:
○Set goals: Achieve real-time detection with high precision and recall.
○Identify key components: Model architecture, dataset, and evaluation metrics.
2.Data Collection & Preparation:
○Use thermal images with labeled objects like 'Powered Vessel', 'Lighthouse', etc.
3.Sprint Execution:
○Implement and train various detection models.
○Monitor training performance and adjust hyperparameters as needed.
4.Review & Retrospective:
○Analyze results: YOLOv5 outperforms other models in terms of mAP and F1
Score.
○Gather feedback: Identify areas for improvement in data handling and model
tuning.
Outcome: A robust real-time object detection system with high accuracy, aiding
autonomous navigation.

12. Use Case: Enhancing Object Classification Models
10
Objective: Improve the accuracy and efficiency of object classification models in identifying
marine objects.
Approach:
1.Sprint Planning:
○Define objectives: Improve model accuracy and speed.
○Select datasets: Use labeled thermal images for training and evaluation.
2.Data Collection & Preparation:
○Collect ~50k labeled thermal images.
○Categorize objects into relevant classes (e.g., 'Sailing boat, 'Powered vessel', etc).
3.Sprint Execution:
○Train various classification models under similar conditions
○Evaluate models using metrics like mAP, precision, recall, and F1 Score.
4.Review & Retrospective:
○Compare performance metrics: YOLOv5 shows higher precision, recall, and F1
Score.
○Plan next sprint: Focus on further tuning existing models or exploring new models.
Outcome: Enhanced model accuracy and efficiency, enabling better object classification in
marine environments.

13. Conclusion and Future Outlook
14
Summary of the importance of effective AI project
management and the evolving role of methodologies like
DDS in the face of advancing AI technologies.
•Effective AI project management is crucial for aligning AI
initiatives with organizational goals and navigating the
complexities of AI development.
•Methodologies like Data Driven Scrum (DDS) play a key
role in addressing the unique challenges of AI projects by
offering flexibility, iterative learning, and enhanced
collaboration.
•The evolving landscape of AI technologies underscores
the need for adaptive and data-driven project
management approaches to maximize the potential of AI
advancements.
•The future outlook for AI project management
emphasizes the importance of continuous innovation,
skill development, and the integration of agile
methodologies like DDS to stay ahead in the rapidly
advancing field of AI.

14. Thank you
15
Ana Stojkovic Knezevic
PM @Protech doo
[email protected]