From the course: Data-Centric AI: Best Practices, Responsible AI, and More
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Challenges faced in deploying and maintaining ML models
From the course: Data-Centric AI: Best Practices, Responsible AI, and More
Challenges faced in deploying and maintaining ML models
- [Instructor] Today we are seeing a growing trend where businesses are increasingly experimenting with machine learning. But let's remember, it's not just about creating a model. There is much more to it. To truly harness the power of ML model and integrate it seamlessly into a core software system, we have to do something more crucial, deploy it into production. So what does this mean? Deploying a machine learning model into production means we make it available to our software systems in a way that they can use it efficiently. This opens up exciting possibilities. Other software systems can send their data to these machine learning models and receive predictions in return. These predictions are then integrated back into our software systems, enriching their capabilities. In a sense, the real magic of ML models happen when we really deploy them. It's the bridge that connects our models to the real world, allowing them…
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