- Learning Salesforce Einstein
- Mohith Shrivastava
- 192字
- 2021-07-02 21:43:58
Drawback of this approach
The preceding approach is not scalable, while it serves as a great experiment to understand the fundamentals of a machine learning process.
From the previous experiment, the following are the conclusions:
- The prediction system uses a larger Dataset and, hence, considering data limits on the Salesforce platform, it's always better to have a big data server collecting the data and forming the model
- There is a need to train the data periodically to get an appropriate model and keep it up-to-date
- Machine learning uses statistical analysis under the hood. In this scenario, we are using the regression model. As an app developer, there is no need to really dig into mathematics although there is no harm in doing so
Google is planning to shut down its PredictionAPI services from June 2018. We have used it for experimental purposes only. Please check the article at https://cloud.google.com/prediction/docs/end-of-life-faq to learn more. Instead, for the future, Google has introduced cloud machine learning services. If you are planning to build machine learning tasks with Google API, you will prefer the machine learning services documented at https://cloud.google.com/products/machine-learning/.
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