Learn how to avoid scope creep and deliver value in your machine learning projects by following these tips on problem definition, project planning, communication, change management, and evaluation.

Scope creep isn’t new, it is often observed outside of ML projects too; here are some tips to combat it. - A robust requirements & scoping team/framework that helps defend the ML teams against additional asks can play the first line of defense to confirm whether those are truly urgent and/or important. - Leaders managing ML teams need to be experts in dynamically categorizing additional asks into buckets like: a.trivial, b.non-trivial but doable, or c.downright unrealistic; this can ensure best bang for buck in agile scenarios. - Pipelining stakeholder asks is a good practice. They need to be coerced into a system that splits demands into multiple iterations consisting: minimum viable product goals, stretch goals, and north-star visions.

How do you prevent scope creep in machine learning projects?

Scope creep is the tendency of a project to expand beyond its original goals, often due to changing requirements, unclear expectations, or lack of communication. It can lead to wasted time, resources, and quality in any kind of project, but especially in machine learning, where data, models, and algorithms are complex and dynamic. How can you prevent scope creep in your machine learning projects and deliver value to your stakeholders? Here are some tips to help you.

Key takeaways from this article

Establish clear success metrics:

Define the problem and success criteria before starting. This helps avoid distractions and ensures all efforts align with the project's goals.### *Implement a robust scoping framework:Use a dedicated team to evaluate additional requests. This ensures only urgent and important changes are prioritized, maintaining project focus.

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1 Define the problem and the success criteria

Before you start any machine learning project, you need to have a clear and specific definition of the problem you are trying to solve and the metrics you will use to measure the success of your solution. This will help you avoid getting distracted by irrelevant or unnecessary features, data sources, or methods that do not contribute to your goal. You should also align your problem definition and success criteria with your stakeholders, such as clients, managers, or users, and get their feedback and approval. This will help you avoid misunderstandings or conflicts later on.

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Younes Sandi

Data Scientist | AI Specialist | Mentor | Recognised Talent in Tech by UK Global Talent Program 🇬🇧
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Success or definition of done might mean different things from different perspectives, for example a data lead might define success in reaching a specific ML performance, let’s say minimum 0.75 and 0.8 in precision and recall respectively if it’s a supervise classification problem while a product manager measures that in terms of business value, let’s say they care about the model uplift and customer retention increase by 15%. It is crucial to understand these requirements and the trade of between them, as most of the time it’s impossible to achieve all these objectives considering project limitations.

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2 Plan your project scope and timeline

Once you have defined the problem and the success criteria, you need to plan your project scope and timeline. This means breaking down your project into manageable tasks, estimating the time and resources needed for each task, and setting milestones and deadlines. You should also prioritize the tasks according to their importance and urgency, and identify the dependencies and risks involved. Planning your project scope and timeline will help you stay focused and organized, and track your progress and performance.

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3 Communicate and document your project

Communication and documentation are essential for preventing scope creep in machine learning projects. You need to communicate regularly and clearly with your team members, stakeholders, and other collaborators, and update them on your project status, achievements, challenges, and changes. You also need to document your project process, decisions, assumptions, results, and feedback, and store them in a central and accessible location. Communication and documentation will help you avoid confusion, errors, duplication, or misalignment in your project, and ensure transparency and accountability.

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4 Manage change requests and expectations

Despite your best efforts, you may encounter change requests or expectations from your stakeholders or yourself during your machine learning project. These may be due to new data, insights, requirements, or opportunities that arise along the way. While some changes may be beneficial or necessary for your project, others may be unrealistic or detrimental. You need to manage these change requests and expectations carefully and critically, and evaluate their impact on your project scope, timeline, quality, and value. You should also communicate the pros and cons of the changes to your stakeholders or yourself, and negotiate a mutually acceptable solution.

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Younes Sandi

Data Scientist | AI Specialist | Mentor | Recognised Talent in Tech by UK Global Talent Program 🇬🇧
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When decided to add a change request it’s a good practice to communicate with stakeholders to understand the level of effort (time, volume, resources, etc), complexity of the task, business value and priority. This way you’ll have a much better idea how to approach the change request.

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5 Review and evaluate your project

Finally, you need to review and evaluate your project regularly and systematically, and compare it with your original problem definition, success criteria, project scope, and timeline. This will help you identify any deviations, gaps, or issues in your project, and take corrective actions if needed. You should also solicit feedback from your stakeholders or yourself, and assess how well your project meets their or your needs and expectations. Reviewing and evaluating your project will help you ensure quality and consistency, and deliver value to your stakeholders or yourself.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rahul Zende

Data Science at Navy Federal Credit Union | Conference Speaker, Panelist & Chair | Technical Reviewer | UW Seattle MSIM | xLinkedIn Top ML Voice | Mentor
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Scope creep isn’t new, it is often observed outside of ML projects too; here are some tips to combat it. - A robust requirements & scoping team/framework that helps defend the ML teams against additional asks can play the first line of defense to confirm whether those are truly urgent and/or important. - Leaders managing ML teams need to be experts in dynamically categorizing additional asks into buckets like: a.trivial, b.non-trivial but doable, or c.downright unrealistic; this can ensure best bang for buck in agile scenarios. - Pipelining stakeholder asks is a good practice. They need to be coerced into a system that splits demands into multiple iterations consisting: minimum viable product goals, stretch goals, and north-star visions.

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Sarah Floris, MS

Senior Data & ML Engineer | dutchengineer.substack.com | Host of Ask A Data Mentor Podcast
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I highly recommend reading the first part of Machine Learning in Action by Ben Wilson; he covers the pitfalls of most projects and how to prevent them.

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Manishankar Singh

Machine Learning Engineer at Tata Consultancy Services
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The best way to prevent scope creep is to have requirement capturing done in a transparent way and available for everyone in team to check in a digital format. Additional discipline can be brought in by enforcing input and acceptance criteria for each additional work. Most of the time scope creep happens because the person giving the requirements does not have substantial depth in ML/data analytics and might be believing in hype without ever doing hands on.

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How do you prevent scope creep in machine learning projects?

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2

3

4

5

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1 Define the problem and the success criteria

2 Plan your project scope and timeline

3 Communicate and document your project

4 Manage change requests and expectations

5 Review and evaluate your project

6 Here’s what else to consider

Machine Learning

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How do you prevent scope creep in machine learning projects?

1

2

3

4

5

6

1 Define the problem and the success criteria

2 Plan your project scope and timeline

3 Communicate and document your project

4 Manage change requests and expectations

5 Review and evaluate your project

6 Here’s what else to consider

Machine Learning

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