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AI Platform provides tools to upload your trained ML model to the cloud, so that you can send prediction requests to the model.
In order to deploy your trained model on AI Platform, you must save your trained model using the tools provided by your machine learning framework. This involves serializing the information that represents your trained model into a file which you can deploy for prediction in the cloud.
Then you upload the saved model to a Cloud Storage bucket, and create a model resource on AI Platform, specifying the Cloud Storage path to your saved model.
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To develop and manage a production-ready model, you must work through the following stages:
Monitor the predictions on an ongoing basis. AI Platform provides APIs to examine running jobs. In addition, various Google Cloud tools support the operation of your deployed model, such as Cloud Logging and Cloud Monitoring.
You must have access to a large set of training data that includes the attribute (called a feature in ML) that you want to be able to infer (predict) based on the other features.
Develop your model using established ML techniques or by defining new operations and approaches.
AI Platform provides the services you need to train and evaluate your model in the cloud. In addition, AI Platform offers hyperparameter tuning functionality to optimize the training process.
Having sourced your data, you must analyze and understand the data and prepare it to be the input to the training process. For example, you may need to perform the following steps:
During training, you apply the model to known data to adjust the settings to improve the results. When your results are good enough for the needs of your application, you should deploy the model to whatever system your application uses and test it.
TensorFlow has several preprocessing libraries that you can use with AI Platform. For example, tf.transform.
In the preprocessing step, you transform valid, clean data into the format that best suits the needs of your model. Here are some examples of data preprocessing:
Before you start thinking about how to solve a problem with ML, take some time to think about the problem you are trying to solve. Ask yourself the following questions:
Machine learning (ML) is a subfield of artificial intelligence (AI). The goal of ML is to make computers learn from the data that you give them. Instead of writing code that describes the action the computer should take, your code provides an algorithm that adapts based on examples of intended be...
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The terminologies might change, the technologies might evolve, but this is the future. And that Future is already here!
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