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How Data Scientists and ML Engineers Can Maintain Accurate Models in Production
So you’ve finished developing your model and you have successfully deployed it into production for use by the business. Job’s done, right?
Unfortunately, it’s not. For many data scientists and ML engineers, it’s the start of a whole new set of challenges like:
- Monitoring the ongoing accuracy of models in production to detect drift.
- Knowing when to retrain models if the environment has changed.
- Iterating and testing new models versus old to ensure the most accurate predictions.
What you’ll learn in this session:
- How to monitor the ongoing accuracy models in production.
- Identifying model drift in your models before it happens.
- When to retrain your models due to environment changes.
- How data science teams can ensure they have the best available models in production.
- Understanding which data is driving model results through basic explainability
- And more!
Wallaroo levels the machine learning playing field:
Easy, Secure and Scalable Deployment
Launch 3X Faster
12.5X Faster Inferencing
80% Lower Server Costs