Source and prepare your data

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:

• Do you have a well-defined problem to solve? Many different approaches are possible when using ML ...

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:

• Join data from multiple sources and rationalize it into one dataset.
• Visualize the data to look f...

To develop and manage a production-ready model, you must work through the following stages:

• Source and prepare your data.
• Develop your model.

• Train an ML model on your data:

• Train model

• Evaluate model accuracy

• Tune hyper...

Develop your model using established ML techniques or by defining new operations and approaches.

Start learning by working through TensorFlow's getting started guide. You can also follow the scikit-learn documentation or the XGBoost documentation to create your model. Then examine some code...

AI Platform provides various interfaces for managing your model and versions, including a REST API, the gcloud ai-platform command-line tool, and...

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...

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.

When training your model, you feed it data for which you already know the value for your target dat...

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.

To test your model, run data through it in...

• Vertex AI Workbench user-managed notebooks are Deep Learning VM Images instances pre-packaged with JupyterLab notebooks and optimized for deep learning data science tasks, from data preparation and exploration to quick prototype development.
• BigQuery is a fully managed data warehouse...

AI Platform provides the services you need to request predictions from your model in the cloud.

There are two ways to get predictions from trained models: online prediction (sometimes called HTTP prediction) and batch prediction. In both cases, you pass input data to a cloud-hosted machine-...

TensorFlow has several preprocessing libraries that you can use with AI Platform. For example, tf.transform.

You can deploy and serve scikit-learn pipelines on AI Platform to apply built-in transforms for training and online prediction. Applying custom transformations is in beta.

You ...

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.

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 s...