The text emphasizes the importance of selling machine learning models beyond just building them. It provides five key insights derived from the author’s documentation experience, including logging experiments, demonstrating performance, describing the model building steps, assessing risks and limitations, and testing data stability. The author outlines their personal experiences in handling complex machine learning projects.
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Building Your Model Is Not Enough — You Need To Sell It
5 tips that can help your models go live in production
When I started in Data Science, my focus was primarily on improving technical skills like programming and model building. After a few years, my interest shifted towards model deployment and MLOps, leading to my transition into Machine Learning Engineering. Public speaking and presentations were always part of the job, especially when conveying results to a non-technical audience. However, things changed last year when I started working on more complex projects with potential reputational or financial risks for the hiring companies.
At this point, models required validation by a committee of both technical and non-technical reviewers before going live in production. This necessitated proper documentation, covering everything from architecture and training methodology to performance reports and experiment history. It meant that having good performance was not enough; I had to convince others, from data scientists to risk assessment specialists, that my models were not only effective but also safe.
In essence, I had to learn how to sell them.
Initiating the process of detailing my models initially served as a crucial requirement for validation, but it swiftly evolved into a routine ingrained in my approach, extending even to personal projects. Within this article, I aim to impart five valuable insights derived from my documentation experience that may assist you in crafting your own.
- Log your experiments
- Show performance and explain metrics
- Describe the model building steps
- Thoroughly assess the risks and the limitations of your model
- Test the stability of your data
This is the foundation of all your documentation. When you start developing a model, you engage in a trail and error process during which you try different kind of pre-processing, model architectures, hyperparameters, and feature engineering.
One of your goals is to show that your model has good performance. But what does that mean? And compared to what?
It’s easier to convince people to use a model when they feel confident in how to implement it and understand how it’s made.
That part can be one of the most important depending on who reads your documentation.
The first time I was asked to do backtesting, I was skeptical but it can be insightful and I now integrate it in my habits.
In wrapping up, I hope the insights shared can be a helpful guide for creating strong machine learning models and making a case for putting them into action. Drawing from personal experiences in handling complex projects with potential risks, I’ve outlined 5 key takeaways:
- Log Your Experiments
- Show Performance and Explain Metrics
- Describe the Model Building Steps
- Thoroughly Assess Risks and Limitations
- Test the Stability of Your Data
If you have any questions, don’t hesitate to leave it in the comments, I’ll do my best to answer you!
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