Summary
1. Summary
SPEAKER: This concludes the module on AI development workflow. Let's do a quick recap. In this module, you learned about the three main stages of the machine learning workflow with the help of the restaurant analogy. In stage 1, data preparation, you uploaded data and applied feature engineering. This translated to gathering our ingredients and then chopping and prepping them in the kitchen. In stage 2, model development, the model was trained and evaluated. This is where you experimented with the recipes and tasted the meal to ensure that it turned out as expected. And in the final stage, model serving, the model was deployed and monitored. This translates to serving the meal to customers and adjusting the menu as more people tried and reviewed the dish. Two ways to build a machine learning model from end to end-- one is through a user interface, like you practiced in the AutoML lab. The other is with code, which you were shown using pre-built SDKs with Vertex AI pipelines. The latter helps you automate the ML pipeline to achieve continuous integration, training, and delivery. Thanks for joining us in the kitchen, where you explored the full recipe from prepping data to serving models. We can't wait to see you build your own ML models with Google Cloud. This concludes your journey learning to build AI with Google. We'll see you soon for a short recap of the entire course.2. Let's practice!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.