What is the simplest route to most basic didactic AI model training and deployment on Google AI Platform using a free tier/trial using a GUI console in a step-by-step manner for an absolute begginer with no programming background?

/ / Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Plain and simple estimators

To begin training and deploying a basic AI model using the Google AI Platform via the web-based GUI, especially as an absolute beginner with no programming background, it is advisable to use Google Cloud’s Vertex AI Workbench and AutoML (now part of Vertex AI) features. These tools are specifically designed for users without coding experience and offer step-by-step, graphical interfaces for building machine learning models. The following guide outlines each phase of this process, focusing on didactic clarity and practical utility.

Step 1: Setting up a Google Cloud Account and Project

1. Sign Up for Google Cloud
– Visit [https://cloud.google.com/](https://cloud.google.com/) and click on “Get started for free.” Google provides a free trial with credits for new users, typically $300 valid for 90 days.
– Follow the prompts to create a Google Cloud account using your Google credentials, entering billing information (you won’t be charged until you exceed the free credits).

2. Create a New Project
– After logging in, open the [Google Cloud Console](https://console.cloud.google.com/).
– Click the project dropdown (top navigation bar) and select “New Project.”
– Name your project (e.g., “ml-demo-project”) and select an organization if applicable. Click “Create.”

3. Enable Billing and APIs
– Ensure billing is enabled for your new project (follow prompts if necessary).
– Navigate to “APIs & Services” → “Library.” Search for “Vertex AI API” and click “Enable.”

Step 2: Preparing Data for Model Training

1. Understanding Dataset Requirements
– AutoML in Vertex AI supports structured data (tabular), image, text, and video.
– For demonstration, use a simple tabular dataset—such as the classic Iris flower dataset or your own CSV data.
– The file should be in CSV format, with columns representing features (inputs) and one column for the target label (what you want to predict).

2. Storing Data in Cloud Storage
– Navigate to “Storage” in the left sidebar of the Cloud Console.
– Click “Create bucket,” assign a unique name (e.g., “ml-demo-bucket”), select the region, and accept defaults for other settings.
– After the bucket is created, click on it, then “Upload files,” and upload your CSV dataset.

Step 3: Accessing Vertex AI and Launching AutoML

1. Open Vertex AI
– In the Cloud Console, navigate to “Vertex AI” in the left sidebar.

2. Creating a Dataset
– Click on “Datasets” under the Vertex AI menu.
– Select “Create” and choose the dataset type (e.g., “Tabular” for CSV data).
– Name your dataset, select the region, then click “Create.”
– After creation, click “Import data,” select “From Cloud Storage,” and specify the path to your CSV file in the format: `gs://[your-bucket-name]/[your-file].csv`.
– Let the platform analyze and import your data. You’ll see a preview where you can verify detected columns and types.

Step 4: Configuring and Training the Model

1. Initiating Model Training
– Once data import completes, click “Train new model.”
– Provide a name for your model (e.g., “iris-predictor”).
– Select the target column (the feature you intend to predict, e.g., “species”).
– Choose a model objective (e.g., “Classification” for predicting categories, “Regression” for predicting numbers).
– For absolute beginners, keep the default feature selection; Vertex AI can automatically select and preprocess features.

2. Advanced Options (Optional)
– You can leave advanced settings at their defaults to ensure simplicity.
– If interested, you may adjust the training budget (measured in node hours) to control how long training runs. The free trial covers modest experiments.

3. Start Training
– Click “Start training.”
– Vertex AI will automatically split your data into training, validation, and test sets, handle preprocessing, build several candidate models, and evaluate them.
– Training typically takes a few minutes to several hours depending on dataset size and complexity.

Step 5: Evaluating the Trained Model

1. Viewing Evaluation Metrics
– After training completes, Vertex AI presents evaluation results, including metrics such as accuracy, precision, recall, F1-score (for classification), or RMSE/MAE (for regression).
– The confusion matrix is shown for classification tasks, helping you understand prediction performance by class.

2. Understanding the Results
– For didactic purposes, review these metrics:
– Accuracy: Proportion of correct predictions out of all predictions.
– Precision: How often the model is correct when it predicts a certain class.
– Recall: Proportion of actual cases of a class that the model correctly identifies.
– Use these to assess if the model is performing adequately for your educational or demonstration needs.

Step 6: Deploying the Model for Prediction

1. Model Deployment
– In the model details screen, click “Deploy to endpoint.”
– Assign a name to the endpoint (e.g., “iris-predictor-endpoint”).
– Keep the default machine type, which is suitable for demo-level usage and is covered by the free trial.
– Click “Deploy.” This process will set up an online endpoint to receive prediction requests.

2. Making Predictions via GUI
– Once the model is deployed, click “Test & use.”
– Enter new data directly in the form (matching the model’s input features) to obtain predictions.
– Example: If using the Iris dataset, input sepal length, sepal width, petal length, and petal width to receive the predicted species.

Step 7: Exploring Model Explainability (Optional, but Didactic)

– Vertex AI offers explainability features that highlight which input features most influenced the model’s prediction.
– Access the “Explain” tab for your model and review which columns the model used most for making predictions.
– This provides valuable insight for learners about how machine learning models make decisions, reinforcing the relationship between input data and outputs.

Step 8: Cleaning Up Resources

– To avoid unnecessary charges, delete deployed endpoints and unused resources after experimentation:
– Go to Vertex AI → “Endpoints,” select your endpoint, and click “Delete.”
– Delete datasets, models, and Cloud Storage buckets as needed.
– Optionally, delete the entire project from the Cloud Console’s project settings.

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Didactic Value and Explanation

This graphical, step-by-step approach to machine learning on Google Cloud’s Vertex AI platform is particularly effective for absolute beginners for several reasons:

1. Intuitive User Interface

The graphical console eliminates the need to write code, making machine learning approachable for users without a technical background. Each action (uploading data, training a model, deploying, and predicting) is performed through guided forms and buttons.

2. Immediate Feedback

Vertex AI provides instant feedback at each phase—whether it is uploading data, verifying columns, evaluating model performance, or making predictions. This allows learners to see the concrete results of their actions at every step.

3. End-to-End Lifecycle

By progressing from data upload to model deployment and prediction in a single interface, learners gain a full overview of the machine learning process, reinforcing a conceptual understanding of the stages involved in AI projects.

4. Didactic Use of Examples

Using well-known datasets, such as the Iris flower classification, enables clear, relatable demonstrations of machine learning principles—such as how input features determine model outputs and how evaluation metrics quantify performance.

5. Transparency and Explainability

Model explainability tools are integrated into the GUI, enabling users to visualize which features most influence predictions. This demystifies the “black box” nature of AI and supports comprehension of model behavior.

6. Safe Experimentation

The free trial and modest resource limits provide a risk-free environment for experimentation. Users can try different datasets, model objectives, or data splits without incurring costs or affecting production systems.

7. Minimal Technical Barriers

This approach does not require knowledge of programming, command-line tools, or complex environment setup. Users interact with menus, forms, and graphical outputs, lowering the barriers to entry for learning machine learning concepts.

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Example Use Case

Consider a beginner interested in predicting whether an iris flower is one of three species based on four measurements (sepal length, sepal width, petal length, and petal width). The steps would be:

– Download the Iris dataset in CSV format from the UCI Machine Learning Repository or Kaggle.
– Upload the CSV to a Cloud Storage bucket.
– Create a Vertex AI tabular dataset and import the CSV.
– Train a classification model by specifying “species” as the target column.
– Review accuracy and confusion matrix after training.
– Deploy the model and enter new flower measurements in the GUI to receive a predicted species.
– Use the explainability tab to see which measurements most influenced the predictions.

This process provides a complete, hands-on introduction to machine learning on Google Cloud—covering data preparation, model building, deployment, and interpretation, all through a GUI suited for those with no programming experience.

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Further Learning Steps

After mastering this workflow, learners can deepen their understanding by:

– Experimenting with different datasets (e.g., Titanic survival prediction).
– Exploring other Vertex AI model types, such as image or text classification.
– Reviewing the automatically generated model reports for deeper insight.
– Gradually introducing basic programming concepts by exploring the “Notebooks” section or exporting trained models for use in Google Colab.
– Watching the “Vertex AI” introductory videos available in the Google Cloud documentation.

When ready, transitioning from GUI-based tools to code-based workflows is facilitated by the familiarity gained with the overall process, terminology, and project structure.

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