From the course: Synthetic Data: Advanced Concepts and Applications
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Machine learning with synthetic data
From the course: Synthetic Data: Advanced Concepts and Applications
Machine learning with synthetic data
- The machine learning development cycle is a roadmap that guides you in creating and improving machine learning models. The cool thing is, is that it helps us make sure our models are accurate and reliable. Of course, you need to train models, but you also need to deploy them to production, as well as monitor and retrain them in a timely fashion. In this lesson, we'll go over the machine learning development cycle and parts where synthetic data can enhance it. After all, it's not only about putting a model into production, but also keeping it there. There are many parts of a model development cycle, including data collection and generation, exploratory data analysis, data annotation, model training, deployment, monitoring, and retraining. Here are some ways synthetic data can improve the cycle and speed up development velocity, which can be roughly defined as the ability to rapidly prototype and iterate on ideas. First…
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