Machine Learning Project Checklist

A Deeper Dive: Planning Your Machine Learning Project with a Detailed Checklist

Last week, we shared a quick 10-step guide to help integrate machine learning (ML) into your lab workflows. We received fantastic feedback from researchers and lab managers eager to dive deeper into how to successfully execute their ML projects. Based on that feedback, we’ve put together a comprehensive project planning checklist to help you get started on the right foot and ensure your ML initiatives run smoothly from start to finish.

1. Project Motivation
Before diving into an ML project, it’s crucial to define why you're starting this journey.

• What problem do you want to solve?

• What goals are linked to this project?
  Answering these questions will help you understand the overall objective and ensure that your team is aligned on the outcomes.

2. Problem Definition
Clearly define the problem you're addressing and how machine learning can provide a solution.

• What specific output do you want to predict?

• What input data do you have for the algorithm?
  For each dataset, you'll need to document:

• The number of rows (or images, videos, recordings, etc.).

• The number of years of historical data available.

• Location: Where is the data saved, and how can it be accessed?

• Generation: How is the dataset created, and how frequently is it updated?

• Limitations/Biases: Are there biases or limitations in the data?

• Protocols: Is there a protocol on how to label or collect the data?

• Relevance: What are the most relevant factors for predicting the output?

• Training examples: How many training examples can you provide?

• Validation datasets: Do separate datasets exist for validation?

3. Performance Measurement
Assess the performance of your ML solution by setting benchmarks and defining the accuracy expectations.

• Do you have a simple benchmark to compare against?

• Do you have a human benchmark to compare against?
  If yes, consider:

• How does it perform?

• Do you have documentation on how it works?

• How will you measure the accuracy of predictions?

• What is the minimum level of accuracy you expect?

• What would a perfect solution look like?

• Are there reference solutions (such as research papers) to guide you?

• Post-processing: Is there any post-processing logic that needs to be applied?

4. Timeline
Set clear deadlines for different phases of the project.

• Are there any deadlines to be aware of?

• When do you need to see the first results?

• When do you want to have a finished solution?

5. Contacts
Establish the key players in your project and ensure good communication throughout.

• Who is responsible for the project (PM)?

• Who can grant access to the datasets?

• Who can help understand the current process and/or the simple benchmark?

• Set up bi-weekly or weekly updates after each sprint to track progress.

6. Existing Datasets
Ensure that you have a clear understanding of the datasets you’re working with.

• Are there specific types of equipment involved in collecting the datasets? For example, smartphones (iPhone 14 Max) or a 16MP camera.

7. External Resources
Identify where your project’s code and resources will be located.

• Define where code & issues are located and how to access them.

• Are there any software requirements? For example, do you need to use a specific cloud provider (AWS/Microsoft/Google) or particular equipment (e.g., phones, desktops)?

By following this checklist, you'll have a clearer picture of how to plan and execute your ML project. With well-defined goals, benchmarks, and resources in place, you can ensure your machine learning project will deliver impactful results for your lab.

Want to learn more about integrating ML into your lab? Schedule a call to discuss how LabTools.AI can streamline your workflows: https://calendly.com/vitality-robotics

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