What is Vertex AI?

Google Cloud Tech
22 May 202107:16

TLDRVertex AI is a comprehensive platform designed to streamline the machine learning workflow for users with varying levels of expertise. It offers tools for data preparation, model training with AutoML or custom options, model evaluation and optimization, and deployment for online and batch predictions. The platform provides a unified environment with a dashboard for managing datasets, training jobs, models, and endpoints, enabling efficient AI innovation and model serving.

Takeaways

  • 📊 Vertex AI is a comprehensive platform designed to streamline the machine learning workflow from data sets to deployed models.
  • 🔍 The platform is essential for utilizing data to make predictions and enhance apps and user experiences across different levels of ML expertise.
  • 📈 It simplifies the ML workflow by providing tools for data ingestion, analysis, transformation, model training, evaluation, optimization, and deployment.
  • 🧠 Vertex AI supports both AutoML for easy model training without coding and custom models for more control over the model's architecture.
  • 🖼️ AutoML is suitable for various data types like images, videos, text files, and tabular data, automating the process of finding the best model for a given task.
  • 🤖 For more complex use cases, custom models allow the use of TensorFlow, PyTorch, and other frameworks, as well as the ability to write your own code.
  • 📊 The platform includes explainable AI, enabling users to understand the factors influencing the model's predictions.
  • 🚀 Vertex AI facilitates model deployment, managing the physical resources and scalable hardware needed for efficient online predictions.
  • 🔧 The console dashboard provides an overview of recent datasets, models, and predictions, and is the central hub for managing the ML workflow.
  • 🔄 The platform supports both online and batch predictions, with endpoints for serving models and tools for traffic splitting and logging.
  • 📚 Upcoming episodes will delve deeper into each step of the machine learning workflow, guiding users through building end-to-end ML solutions.

Q & A

  • What is Vertex AI?

    -Vertex AI is a platform that provides tools for every step of the machine learning workflow, catering to varying levels of machine learning expertise from novice to expert. It simplifies the process of building machine learning models, making predictions, and improving applications and user experiences.

  • What are the typical steps in a machine learning workflow?

    -The typical machine learning workflow includes defining a prediction task, ingesting and analyzing data, transforming it, creating and training a model, evaluating its efficiency and optimization, and finally deploying the model to make predictions.

  • How does Vertex AI simplify the data preparation process?

    -Vertex AI simplifies data preparation by offering managed datasets. Users can create datasets by importing data using the console or API and can label and annotate data directly from within the console.

  • What are the model training options available on Vertex AI?

    -Vertex AI provides two model training options: AutoML and custom models. AutoML is suitable for users with varying machine learning expertise and works well for images, videos, text files, and tabular data. Custom models are ideal for users who want more control over their model's architecture and are compatible with frameworks and code written in TensorFlow, PyTorch, and similar.

  • What is explainable AI in the context of Vertex AI?

    -Explainable AI in Vertex AI allows users to understand the factors influencing their model's predictions. It helps to dive deeper into the model to gain insights into how it's making decisions.

  • How does Vertex AI handle model deployment?

    -Vertex AI handles model deployment by providing endpoints for online predictions. It includes the necessary physical resources and scalable hardware to scale the model for lower latency and online predictions. Users can make predictions using the command line interface, console UI, or SDK and APIs.

  • What can users do in the Vertex AI console?

    -In the Vertex AI console, users can view the dashboard with recent datasets and models, create datasets, manage notebook instances, view and create training jobs, import and manage models, create endpoints for serving models, and make batch predictions from Cloud Storage.

  • How does Vertex AI support different data types?

    -Vertex AI supports various data types including images, tabular data, text, and videos. Users can create datasets based on their prediction tasks and data types.

  • What frameworks and languages are compatible with custom containers on Vertex AI?

    -Custom containers on Vertex AI allow users to train models built with any framework or language. Users can package their training application code in a Docker container, push it to Container Registry, and run the training on Vertex.

  • How can users optimize their model training on Vertex AI?

    -Users can optimize their model training on Vertex AI by utilizing GPUs to accelerate the training process and applying hyperparameter tuning to find the best model configuration.

  • What are the benefits of using Vertex AI for machine learning projects?

    -Using Vertex AI for machine learning projects provides a streamlined workflow from data management to predictions, support for different expertise levels, a range of model training options, explainable AI for deeper insights, and efficient model deployment with scalable resources.

Outlines

00:00

🤖 Introduction to AI Simplified and Vertex AI

The paragraph introduces the concept of AI Simplified, a journey from datasets to deployed machine learning models. It emphasizes the importance of using data for predictions to improve applications and user experience. The speaker, Priyanka, acknowledges the varying levels of machine learning expertise in teams and presents Vertex AI as a solution that caters to both novices and experts. Vertex AI is described as a comprehensive platform that facilitates every step of the machine learning workflow, including data preparation, model training, evaluation, optimization, and deployment. The paragraph outlines the typical machine learning workflow and explains how Vertex AI simplifies these processes, offering tools for data ingestion, analysis, transformation, and model training through AutoML or custom models. It also touches on the capabilities of Vertex AI for model assessment, optimization, and explainable AI, highlighting the deployment process and the resources required for online predictions.

05:03

🛠️ Custom Model Training and Vertex AI Features

This paragraph delves into the specifics of custom model training with Vertex AI, presenting the options available for teams with varying expertise. It discusses the use of pre-built or custom containers for training models with different frameworks, such as TensorFlow, PyTorch, scikit-learn, and XGBoost. The paragraph explains that users can provide their code as a Python package or use Docker containers for training with any framework or language. It also mentions the acceleration of training with GPUs and the application of hyperparameter tuning. The Models tab is highlighted as a place to view and import models, including those trained outside of Google Cloud. The process of creating endpoints for serving models is detailed, explaining how they can auto-scale based on traffic and handle batch predictions. The paragraph concludes with a mention of the Batch Predictions tab for making predictions on data from Cloud Storage and a teaser for future episodes that will explore the machine learning workflow in more depth.

Mindmap

Keywords

💡Vertex AI

Vertex AI is a platform designed to streamline the machine learning workflow. It provides tools for every step of the process, from data preparation to model deployment, catering to varying levels of machine learning expertise. In the context of the video, Vertex AI is presented as a solution that simplifies the machine learning journey, making it accessible to both novices and experts by offering both AutoML for easy model creation and custom model training for more control.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable systems to learn from and make predictions or decisions based on data. In the video, machine learning is the core process that Vertex AI facilitates, allowing users to create models that can predict outcomes based on ingested data, with the aim of improving applications and user experiences.

💡AutoML

AutoML, or Automated Machine Learning, is a feature within Vertex AI that simplifies the process of model creation for users with varying levels of expertise. It automates the selection of the best model for a given task, eliminating the need for users to write model code. This allows even novice users to train high-quality models with minimal effort, while still providing the flexibility for experts to use custom models when more control is desired.

💡Custom Models

Custom models refer to machine learning models that are built with a specific framework or architecture chosen by the user. These models allow for greater control over the model's structure and are suitable for users with more advanced machine learning expertise. In the context of Vertex AI, custom models are used when users want to write their own code, using frameworks like TensorFlow or PyTorch, and train their models with the platform's resources.

💡Data Preparation

Data preparation is the process of ingesting, analyzing, and transforming data to make it suitable for creating machine learning models. This is a critical step in the machine learning workflow as the quality and relevance of the data directly impact the performance of the models. In the video, data preparation is highlighted as a key component of the Vertex AI platform, where users can manage datasets and perform tasks like labeling and annotating data within the console.

💡Model Training

Model training is the process of teaching a machine learning model to make predictions or decisions based on the data it has been fed. This involves adjusting the model's parameters until it can accurately recognize patterns or relationships within the data. In the context of Vertex AI, model training can be done through AutoML for simplicity or with custom models for more complex requirements, using various frameworks and architectures.

💡Explainable AI

Explainable AI refers to the ability to understand the logic behind the decisions, predictions, or outcomes produced by an AI model. It is a critical component in ensuring that AI systems are transparent and trustworthy. In the video, explainable AI is mentioned as a feature of Vertex AI that allows users to dive deeper into their models to understand the factors influencing the model's predictions, thereby enhancing the model's interpretability and accountability.

💡Deployment

Deployment in the context of machine learning refers to the process of making a trained model accessible for use in making predictions or decisions. This involves setting up the necessary infrastructure and resources to serve the model's predictions in real-time or in batches. In the video, deployment on Vertex AI involves creating endpoints and allocating compute resources to serve the model for online predictions, ensuring scalability and low latency.

💡Endpoints

Endpoints in machine learning are points of access where the model's predictions can be made. They are the interfaces through which the model's services are provided, allowing for online predictions to be made in real-time. In the context of Vertex AI, creating an endpoint is how users serve their models for online predictions, with the ability to auto-scale resources based on traffic and manage model logs.

💡Batch Predictions

Batch predictions refer to the process of making predictions on a large set of data at once, rather than one data point at a time. This is useful for analyzing and making predictions on data that has been stored, such as historical data or large datasets. In the video, batch predictions on Vertex AI involve making predictions on a batch of data from Cloud Storage, which can be done in addition to online predictions served through endpoints.

💡SDK and APIs

SDK stands for Software Development Kit, and APIs are Application Programming Interfaces. Both are tools used by developers to build software applications and integrate systems. In the context of Vertex AI, the SDK and APIs allow users to programmatically interact with the platform, manage models, and retrieve predictions. This enables the seamless integration of Vertex AI's machine learning capabilities into other applications or workflows.

Highlights

Vertex AI is a platform designed to streamline the machine learning workflow for users with varying levels of expertise.

It provides tools for every step of the machine learning process, from data preparation to model deployment.

Vertex AI simplifies data ingestion, analysis, and transformation using managed datasets.

Users can create datasets by importing data through the console or API and label and annotate data within the platform.

AutoML is offered for users with limited machine learning expertise, requiring no model code.

Custom model training is available for users seeking more control over their model's architecture.

Custom models support TensorFlow, PyTorch, scikit-learn, and XGBoost frameworks.

Vertex AI allows for model assessment, optimization, and the use of explainable AI to understand the factors behind predictions.

Deployed models can serve online predictions using the API or console, with scalable hardware resources.

Undeployed models can still be used for batch predictions, offering flexibility for different use cases.

The Vertex AI console provides a dashboard for managing datasets, training jobs, models, and endpoints.

The platform supports various data types including image, tabular, text, and video datasets.

Custom notebook instances can be created within the console, with customizable environments and GPUs.

AutoML Edge optimizes models specifically for deployment on edge devices.

Custom Containers enable training models built with any framework or language using Docker containers.

Hyperparameter tuning can be applied to accelerate training and improve model performance.

Endpoints can be created for serving models, with the ability to auto-scale resources based on traffic.

Batch Predictions tab allows for making predictions on data from Cloud Storage.

The platform aims to support the entire machine learning workflow, from data management to making predictions.

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