Building and training ML models with Vertex AI

Google Cloud Tech
31 May 202112:36

TLDRIn this episode of AI Simplified, Priyanka Vergadia discusses building and training machine learning models with Vertex AI. The video covers the two main approaches: AutoML for creating high-quality models with minimal effort, and custom model training for specific requirements or mixed input types. The process of creating a credit card fraud detection model using AutoML is demonstrated, from data analysis to model evaluation and deployment. The importance of feature importance and model explainability is highlighted, along with the steps to get predictions from the deployed model via UI and API.

Takeaways

  • 🚀 Vertex AI offers tools for every step of the machine learning workflow, including data management, model training, evaluation, deployment, and prediction.
  • 📊 Managed data sets in Vertex AI support various data types such as image, tabular, text, and video, which are integral to model building and training.
  • 🤖 AutoML is a convenient option for creating high-quality custom models with minimal effort and machine learning expertise, suitable for basic use cases.
  • 🛠️ Custom model training is recommended when the use case doesn't fit supported AutoML offerings, requires specific model architectures, or involves mixed input types.
  • 🔍 The choice between AutoML and custom model training depends on factors like the use case, team's ML expertise, and project requirements.
  • 🏗️ When building models with Vertex AI, one can start with a basic AutoML model as a baseline or proof of concept and later refine it with custom models.
  • 📈 The credit card fraud detection example demonstrates the process of creating a dataset, selecting AutoML, defining the objective, and training the model.
  • 📊 Evaluation metrics like the confusion matrix and feature importance help in understanding the model's performance and making informed decisions for improvements.
  • 🔄 Feature importance is a type of explainable AI that provides insights into the model's decision-making process and can be used to refine the model.
  • 📡 Deploying the model involves creating an endpoint in Vertex AI, which can have multiple endpoints with traffic split between them for load balancing and A/B testing.
  • 🔮 Model predictions can be obtained through the UI for quick testing or via a REST API for dynamic predictions, with the latter requiring the use of Vertex AI SDK in a notebook instance.

Q & A

  • What is the primary focus of the Vertex AI platform?

    -The primary focus of the Vertex AI platform is to provide tools for every step of the machine learning workflow, including managing datasets, training models, evaluating, deploying, and making predictions.

  • What types of datasets are supported by Vertex AI for managed datasets?

    -Vertex AI supports managed datasets for image, tabular, text, and video data.

  • What are the two main options for training models on Vertex AI?

    -The two main options for training models on Vertex AI are using AutoML, which requires minimal effort and machine learning expertise, and writing custom model training code, which allows for running custom training applications in the cloud.

  • In which situations should one consider using AutoML over custom model training?

    -One should consider using AutoML if their use case fits a supported offering, such as image classification, object detection, text classification, sentiment analysis, tabular regression, and classification, and if they have limited machine learning expertise or a small team.

  • What are some technical factors that might lead to choosing custom model training over AutoML?

    -Technical factors for choosing custom model training include specific requirements for the underlying model, such as needing control over the model's architecture, framework, or exported model assets, or if the model needs to be built in a specific framework like TensorFlow or PyTorch.

  • How does the Vertex AI platform handle data split for training and testing?

    -Vertex AI can automatically handle the data split, but users can also specify a specific train-test split manually when uploading their data or choose to have the data split chronologically if it has a time column.

  • What is the significance of the AUC PRC option in training options?

    -The AUC PRC option is significant when dealing with imbalanced datasets, as it maximizes precision-recall for the less common class, helping the model to classify the minority class more accurately.

  • Why is the confusion matrix a better measure of performance for imbalanced datasets?

    -The confusion matrix is a better measure of performance for imbalanced datasets because it shows the percentage of examples from each class in the test set that the model predicted correctly, providing a more accurate understanding of the model's performance rather than relying solely on overall accuracy.

  • How can feature importance help improve a model's performance?

    -Feature importance can help improve a model's performance by identifying the most significant features that contribute to the model's predictions. This information can be used to remove less important features or combine significant features into a feature cross, potentially enhancing the model's predictive capabilities.

  • What are the steps to create and deploy an endpoint in Vertex AI?

    -To create and deploy an endpoint in Vertex AI, one needs to go to the Deploy and Test tab on the model page, click 'Deploy to Endpoint', give the endpoint a name, select a machine type for deployment, and click 'Deploy'. Once the endpoint is deployed, it can be used for making predictions.

  • How can model predictions be obtained from the deployed endpoint?

    -Model predictions can be obtained from the deployed endpoint either through the UI by testing the model with provided or custom input values, or by making a REST API call dynamically, which can be done through a notebook with the Vertex AI SDK installed.

Outlines

00:00

📊 Introduction to AI Simplified and Vertex AI Overview

The paragraph introduces Priyanka Vergadia, the host of AI Simplified, a series focused on leveraging data effectively through machine learning. The context is set with an imaginary company looking to build a machine learning workflow with the help of Vertex AI, a comprehensive suite of tools for managing datasets, training, evaluating, deploying, and predicting with machine learning models. The host recaps the previous episode on managed datasets and transitions into discussing model building and training. The advantages of using managed datasets are highlighted, and two training options are presented: AutoML for creating high-quality models with minimal effort, and custom model training for more specific requirements. The paragraph concludes with a guide on when to choose AutoML over custom model code, based on factors like use case compatibility, technical needs, and team expertise.

05:01

🔍 Training Models with AutoML for Credit Card Fraud Detection

This paragraph delves into the specifics of training models using Vertex AI's AutoML capabilities. A real-world scenario of credit card fraud detection is used as an example. The host explains the process of creating a dataset from the BigQuery public datasets and outlines the steps to analyze data and select the objective of the model, which is classification in this case. The AutoML process is chosen for its suitability to the use case, and detailed instructions are provided on naming the model, selecting the target column, and specifying advanced options such as train-test split and choosing the AUC PRC metric to handle data imbalance. The paragraph also touches on compute and pricing considerations, the importance of model evaluation using metrics like confusion matrix and feature importance, and the potential insights gained from explainable AI. The steps to create an endpoint in Vertex AI and deploy the model are also covered, providing a comprehensive guide on model deployment and readiness for predictions.

10:02

🔧 Retrieving Model Predictions and API Integration

The final paragraph focuses on the methods available for obtaining predictions from the trained model. It begins by demonstrating the use of the UI for quick testing and validating the deployed endpoint. The host then transitions to explaining the process of making predictions via a REST API, which is essential for dynamic predictions. This section includes a walkthrough of setting up a notebook, installing the Vertex AI SDK, and using the endpoint to make prediction requests. The paragraph concludes by summarizing the key takeaways from the episode, highlighting the understanding of when to use AutoML versus custom training. It also teases the next episode, which will cover building and training a custom model, and encourages audience engagement through comments, likes, and subscriptions.

Mindmap

Keywords

💡Machine Learning Workflow

The machine learning workflow refers to the series of steps involved in creating, training, and deploying machine learning models. In the context of the video, it emphasizes the importance of Vertex AI in facilitating every stage of this workflow, from managing datasets to making predictions with the model.

💡AutoML

AutoML, or Automated Machine Learning, is a technology that enables users to create high-quality, custom machine learning models with minimal effort and machine learning expertise. It streamlines the model development process by automating certain aspects of feature engineering, model selection, and hyperparameter tuning.

💡Custom Model Training Code

Custom model training code refers to the process of writing and implementing specific algorithms and architectures tailored to a particular machine learning task. This approach gives users more control over the model's structure, framework, and exported assets, but requires more in-depth machine learning knowledge and expertise.

💡Managed Datasets

Managed datasets are curated collections of data that are hosted and maintained by a cloud service, such as Vertex AI. These datasets can contain various types of data, like images, text, or tabular data, and they are designed to be easily accessible and integrated into the machine learning workflow.

💡Credit Card Fraud Detection

Credit card fraud detection is a specific application of machine learning where models are trained to identify and flag potentially fraudulent transactions. This is typically a binary classification problem where the model learns to distinguish between fraudulent and non-fraudulent transactions based on transaction features.

💡AUC PRC

AUC PRC, or Area Under the Precision-Recall Curve, is a performance metric used in machine learning to evaluate models, especially in imbalanced datasets. It focuses on the precision and recall of the positive class, providing a better understanding of the model's ability to correctly identify the less frequent events, such as fraudulent transactions.

💡Feature Importance

Feature importance is a measure in machine learning that indicates the relative contribution of each feature in making predictions. It helps to understand which features have the most significant impact on the model's decisions, providing insights into the model's behavior and aiding in model optimization.

💡Endpoints

In the context of machine learning and Vertex AI, an endpoint refers to a deployed model that can be accessed for making predictions. Endpoints are created within Vertex AI and can handle incoming prediction requests, processing them and returning the model's output.

💡Model Evaluation Metrics

Model evaluation metrics are quantitative measures used to assess the performance of a machine learning model. They provide insights into how well the model is making predictions, often including metrics like accuracy, precision, recall, and F1 score, which are crucial for understanding the model's effectiveness.

💡REST API Call

A REST (Representational State Transfer) API (Application Programming Interface) call is a method of communication between different software systems, allowing them to request and exchange data in a standardized way. In the context of machine learning, a REST API call can be used to send prediction requests to a deployed model endpoint and receive the model's predictions.

Highlights

Vertex AI provides tools for every step of the machine learning workflow.

Managed data sets in Vertex AI support image, tabular, text, and video data.

There are two options to train models: AutoML and custom model training code.

AutoML is ideal for basic use cases and requires minimal machine learning expertise.

Custom model training is suitable for specific requirements and mixed input types.

The choice between AutoML and custom model code depends on the use case and team expertise.

AutoML is a good starting point for teams with limited machine learning experience.

For a quick initial model development, AutoML is recommended.

Custom models allow control over the model's architecture, framework, and exported assets.

The credit card fraud detection dataset on BigQuery public datasets is used for demonstration.

The data upload process is explained, including analyzing data in rows and columns.

AutoML is selected for training with the objective of classification.

The importance of selecting the correct target column and handling imbalanced data is discussed.

Advanced options for train-test split and compute hours budget are covered.

Model evaluation metrics, including confusion matrix and feature importance, are explained.

Creating an endpoint in Vertex AI for model deployment is demonstrated.

Options for getting model predictions via UI and API are presented.

The process of making a prediction request using the Vertex AI SDK in a notebook is outlined.

The significance of feature importance in explainable AI is highlighted.

The next episode will cover building and training a custom model in Vertex AI.

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