Strategies for Ensuring Machine Learning Model Success
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Understanding Post-Development Testing for ML Models
Have you ever witnessed machine learning implementations not meeting expectations? It's a common challenge. I've received numerous urgent calls from teams whose models boast a 98% accuracy in development but yield questionable predictions in real-world applications.
Did they create a flawed model? Possibly. However, the central issue often lies in a skipped step—post-development testing (PDT).
What is Post-Development Testing (PDT)?
Post-development testing refers to the crucial phase where a machine learning model, after its development, is tested using actual data and sometimes real users, all prior to its deployment.
Why is PDT Crucial?
PDT is vital for two main reasons: it ensures the model functions as intended in practice (model success) and confirms that it meets business objectives (business success).
Model Success
Let’s first delve into model success. When models transition from development to production, a decrease in performance is typical. However, if the drop is excessive, it signals a significant issue that must be addressed.
For example, take a fraud detection model that shows a 97% accuracy during development. If real data testing reveals only 50% accuracy, this discrepancy is unacceptable from a business perspective.
Several factors could contribute to such a gap between development and production performances. Missing values in the real data, a shift in data distribution, or the model's tendency to overfit can all lead to poor predictions when encountering unfamiliar patterns.
PDT helps identify these issues. Rather than assuming flawless performance, it allows you to validate the model's effectiveness, giving you the opportunity to make necessary adjustments before broader release.
Business Success
Ultimately, a machine learning model must fulfill a specific business need, whether that's automating processes, enhancing productivity, or minimizing human error. Progress toward these goals defines business success.
The experimentation phase during PDT enables verification of business objectives. Even if the model isn’t perfect yet, PDT provides insight into whether the direction is correct concerning business goals.
Consider the earlier fraud detection model intended to boost the productivity of customer support agents. If it fails to do so even after addressing model issues, it may be due to user interface challenges or network delays affecting timely access to results. Such factors can influence decisions on whether to invest further resources in operationalizing the model.
Getting Started with Post-Development Testing
If you haven't yet incorporated PDT into your AI projects, here are four essential tips for initiating the process:
- Test Early
Don't wait for an ideal model. If the development performance is satisfactory, initiate testing to evaluate real-world effectiveness. Delaying this step can lead to discovering issues that may require extensive reworking.
- Utilize Appropriate Production Metrics
Metrics suitable for development may not be applicable in production. For instance, while precision and recall might work for assessing a recommendation system during development, in production, you might track user engagement through click-through rates.
- Adopt a Multi-Faceted Evaluation Approach
PDT is not solely about evaluating model performance; it’s also a chance to assess relevant business metrics. Ensure that both model and business metrics are monitored. If the model performs well but business metrics do not improve, it’s crucial to investigate and resolve underlying issues.
- Iterate and Reassess
As you identify problems during PDT, address them and reassess until you achieve a satisfactory level of accuracy, improvement in business metrics, or user satisfaction. Once you reach a point of diminishing returns, or feel confident in the results, it's time for full deployment.
Exploring Your Experience with PDT
Reflecting on your AI projects, have you prioritized post-development testing? Did it prove beneficial? If not, what changes would you or your team consider before deployment? Share your thoughts below.
Kavita Ganesan is an AI strategist, consultant, and author of The Business Case For AI. For more insights like this, visit www.Kavita-Ganesan.com.
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