Accelerate AI Training with Built-in Algorithms

Table of Contents:

1. Introduction
2. Using AI Platform Training
3. Setting up the Training Job
   • Selecting an Algorithm
   • Specifying the Training Data
   • Setting Algorithm Arguments
   • Finalizing the Training Job
4. Monitoring the Training Job
5. Deploying the Model
6. Conclusion
7. Additional Resources

Using AI Platform Training to Train and Deploy Machine Learning Models

AI Platform Training, offered by Google Cloud, provides a convenient way to train and deploy machine learning models without the need to write any code. This feature is particularly useful for individuals who want to leverage the power of machine learning algorithms without possessing extensive programming skills.

1. Introduction

In this article, we will explore how to use AI Platform Training's built-in algorithms to train and deploy a machine learning model. We will Delve into the process step-by-step, discussing the various options available along the way.

2. Using AI Platform Training

AI Platform Training acts as a wrapper around AI Platform, bridging the gap between AutoML and full custom training jobs. By utilizing the built-in algorithms provided by AI Platform Training, users can train models without the need to write a single line of code.

3. Setting up the Training Job

To begin using AI Platform Training, we first need to decide what Type of model we want to build. In this case, we will be working with the US Census data for a binary classification problem. Our goal is to predict whether a given household income will be above or below $50,000 a year.

Selecting an Algorithm

AI Platform Training offers a range of algorithms, primarily focused on structured data, but also including a few options for image data. The structured data algorithms currently available are XGBoost, Linear Learner, and the Wide and Deep Learner. Each algorithm serves a specific purpose and has unique characteristics.

Specifying the Training Data

Before specifying the training data, it should be uploaded to Google Cloud Storage and formatted as a headerless CSV file. The prediction target column must be the first column in the CSV file. If necessary, some light preprocessing may be required to ensure the dataset is in the appropriate format.

AI Platform Training allows for the specification of validation data and test data, either as separate files or as a percentage of the training data. When using percentages, it is important to ensure that the representative metrics accurately reflect the reality of the data. If separate files are used, it is crucial that they are representative of the same reality.

Setting Algorithm Arguments

Each algorithm provided by AI Platform Training has a set of default values for its parameters. However, users have the flexibility to customize these parameters Based on their specific needs. Additionally, many of these parameters can be utilized for hyperparameter tuning through the HyperTune feature offered by Google Cloud Platform.

Finalizing the Training Job

Once all the necessary details have been specified, the training job can be started. Users need to provide a job ID, select a region, and choose a Scale tier. The training process will commence, and users can monitor the progress using the provided tools.

4. Monitoring the Training Job

While the model is training, users have the flexibility to engage in other activities. They can monitor the resource utilization and view details of the training job, including links to Stackdriver Logging for additional analysis. This time can also be utilized to start Parallel training jobs with different models or parameter ranges.

5. Deploying the Model

Once the training is complete, users have the option to deploy the model directly from the AI Platform Training interface. By clicking the "Deploy Model" button, the exported model from Google Cloud Storage will be deployed, allowing users to make predictions by calling the REST API associated with their deployed model.

6. Conclusion

AI Platform Training provides a user-friendly approach to training and deploying machine learning models. By leveraging built-in algorithms, users can achieve high-quality results without needing to write a single line of code. The flexibility and customization options offered by AI Platform Training make it an ideal choice for both beginners and experienced practitioners.

7. Additional Resources

For more details and examples on using AI Platform Training to train and deploy machine learning models, please refer to the following resources:

• Link 1
• Link 2