What is Vertex AI?

Google Cloud Tech
22 May 202107:16

TLDRVertex AI is a comprehensive platform designed to streamline the machine learning workflow, catering to 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 interface for managing datasets, training jobs, and model endpoints, enabling efficient AI innovation and model serving.

Takeaways

  • 📊 Vertex AI is a platform designed to streamline the machine learning workflow from data sets to deployed models.
  • 🧠 It caters to teams with varying levels of machine learning expertise, from novices to experts.
  • 📈 The typical machine learning workflow involves data ingestion, analysis, transformation, model creation, training, evaluation, optimization, and deployment.
  • 🔄 Vertex AI simplifies data preparation with managed data sets and tools for data labeling and annotation within the console.
  • 🤖 AutoML is a feature that allows users to train models without writing model code, finding the best model for a given task automatically.
  • 💡 Custom models are suitable for users who want more control over their model's architecture and are compatible with frameworks like TensorFlow and PyTorch.
  • 📊 After training, models can be assessed, optimized, and explained using explainable AI to understand the factors influencing predictions.
  • 🚀 Deployment with Vertex AI includes managing physical resources and scalable hardware for online predictions with low latency.
  • 🌐 The Vertex AI console provides a dashboard for managing datasets, training jobs, models, endpoints, and batch predictions.
  • 🔧 Users can create customized notebook instances and training jobs with different environments and GPUs.
  • 🔄 The platform supports various data types including images, tabular data, text, and videos, and allows for custom model training and prediction.

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 streamlines the process of building machine learning models, from data preparation to deployment for predictions.

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

    -The typical machine learning workflow involves 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 phase?

    -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 two model training options provided by Vertex AI?

    -Vertex AI provides two model training options: AutoML, which automates the process of finding the best model for a given task without requiring users to write model code, and custom models, which allow for more control over the model's architecture and are suitable for frameworks and code that users want to write themselves.

  • What is AutoML and how does it benefit users with varying machine learning expertise?

    -AutoML (Automated Machine Learning) is a feature of Vertex AI that automates the process of model selection and training. It benefits users, even those with less machine learning expertise, by finding the best model for a given task, thus reducing the need for manual coding and expertise.

  • How does Vertex AI support explainable AI?

    -Vertex AI supports explainable AI by allowing users to assess, optimize, and understand the signals behind their model's predictions. This feature helps in gaining insights into which factors are influencing the model's predictions.

  • What are the physical resources and scalable hardware provided by Vertex AI for model deployment?

    -Vertex AI provides the necessary physical resources and scalable hardware to scale the model for lower latency and online predictions. This ensures that the deployed model can handle high traffic and serve predictions efficiently.

  • How can users interact with the deployed model?

    -Once a model is deployed, users can obtain predictions using the command line interface, console UI, SDK, or APIs. This allows for both online and batch predictions depending on the user's needs.

  • What types of data are supported in Vertex AI?

    -Vertex AI supports various data types including images, tabular data, text, and videos. This versatility allows users to work with a wide range of data formats for their machine learning tasks.

  • How can users create customized notebook instances in Vertex AI?

    -In the Notebook section of the Vertex AI console, users can create customized notebook instances with the environment and GPUs of their choice. This provides a flexible and personalized workspace for users to develop and test their machine learning models.

  • What are the different ways to train models in Vertex AI?

    -Models can be trained in Vertex AI using AutoML for minimal effort, AutoML Edge for models optimized for edge devices, and Custom Training for models built with any framework via pre-built or custom containers. This variety ensures that users have the flexibility to choose the training method that best suits their needs.

Outlines

00:00

🌟 Introduction to AI Simplified and Vertex AI

This paragraph introduces Priyanka, the host of AI Simplified, and sets the stage for discussing the journey from datasets to deployed machine learning models. It emphasizes the importance of using data for predictions to improve apps and user experiences. The paragraph outlines the challenges faced by teams with varying levels of machine learning expertise and introduces Vertex AI as a solution that provides tools for every step of the machine learning workflow, catering to both novices and experts. The typical machine learning workflow is briefly explained, highlighting the steps from data ingestion to model deployment. The paragraph concludes with an overview of how Vertex AI simplifies this workflow, touching on data preparation, model training options (AutoML and custom), and the use of explainable AI to understand model predictions. The deployment process, including the physical resources needed for online predictions, is also discussed.

05:03

🛠️ In-Depth Look at Vertex AI's Machine Learning Workflow

The second paragraph delves deeper into the specifics of Vertex AI's machine learning workflow. It starts by discussing the model training options available, including AutoML for common use cases like images, videos, text files, and tabular data, and custom models for scenarios requiring more control over the model's architecture. The paragraph explains that AutoML simplifies the process by handling model code, while custom models allow for using TensorFlow, PyTorch, and other frameworks. It also mentions the ability to assess, optimize, and understand models using explainable AI. The deployment process is further elaborated, with an emphasis on the physical resources and scalability for online predictions. The paragraph then provides a tour of the Vertex AI console, detailing the dashboard, data management, model training options, and the creation of endpoints for online predictions. It concludes by inviting the audience to engage in discussions about their machine learning use cases and workflows.

Mindmap

Keywords

💡Vertex AI

Vertex AI is a platform designed to streamline the machine learning workflow. It offers tools for every step of the process, from data preparation to model deployment, catering to users with varying levels of machine learning expertise. In the context of the video, Vertex AI is presented as a solution that simplifies the complex process of building, training, and deploying machine learning models, making AI innovation more accessible to teams with diverse skill sets.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable computers to learn from and make predictions based on data. In the video, machine learning is the central theme, with the focus on how Vertex AI facilitates the creation and deployment of machine learning models. The process includes data ingestion, analysis, model training, evaluation, and deployment for predictions.

💡Data Preparation

Data preparation is the process of cleaning, transforming, and organizing data to make it suitable for model training in machine learning. This step is crucial as the quality of data directly impacts the performance of the models. In the video, data preparation is discussed as a part of the machine learning workflow within Vertex AI, where users can create and manage datasets, label and annotate data through the console or API.

💡AutoML

AutoML, or Automated Machine Learning, is a feature within Vertex AI that enables users to train high-quality models without needing to write model code. AutoML automates the process of selecting the best model for a given task, making it accessible for users with less machine learning expertise. It is particularly useful for use cases involving images, videos, text files, and tabular data.

💡Custom Models

Custom models refer to machine learning models that are built with specific frameworks and architectures defined by the user. This option in Vertex AI is designed for users who want more control over their model's architecture. It is suitable for scenarios where users have a deep understanding of machine learning and wish to write their own code, using frameworks like TensorFlow or PyTorch.

💡Explainable AI

Explainable AI is the ability to understand the factors and reasoning behind a machine learning model's predictions. It provides insights into how the model is making decisions, which is essential for building trust and ensuring the model's fairness and transparency. In the context of the video, explainable AI is a feature of Vertex AI that allows users to dive deeper into their models and understand the signals that influence predictions.

💡Deployment

Deployment in the context of machine learning refers to the process of making a trained model available for use, typically by integrating it into an application or service. In Vertex AI, deployment involves allocating physical resources and scalable hardware to ensure the model can handle online predictions with low latency. The platform allows for both online and batch predictions, with the ability to scale the model as needed.

💡Endpoints

Endpoints in the context of Vertex AI are the access points through which machine learning models are served for making predictions. Each model can have multiple endpoints, allowing for the management of compute resources and the ability to scale based on traffic. Endpoints are crucial for online predictions and are created as part of the model serving process.

💡Batch Predictions

Batch predictions refer to the process of making predictions on a large set of data in one go, as opposed to single predictions made in real-time. This is useful for analyzing historical data or making predictions for a group of data points at once. In Vertex AI, batch predictions can be made from Cloud Storage, allowing for efficient processing of large datasets.

💡SDK and APIs

SDK stands for Software Development Kit, and APIs are Application Programming Interfaces. Both are tools that developers use to build applications or integrate functionalities into existing systems. In the context of Vertex AI, SDKs and APIs are used to interact with deployed models for making predictions, either through the command line interface or programmatically within applications.

💡Console UI

Console UI refers to the graphical user interface of a software or platform, where users can interact with the system and perform tasks. In Vertex AI, the console UI is the dashboard where users can manage datasets, training jobs, models, and endpoints. It provides a visual representation of the machine learning workflow and allows for easy navigation through the various components of the platform.

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 the ingestion, analysis, and transformation of data using managed datasets.

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

AutoML is offered as an option for model training, which requires no coding and automatically finds the best model for the task.

Custom model training is available for users who want more control over their model's architecture and is compatible with TensorFlow, PyTorch, and other frameworks.

After training, models can be assessed, optimized, and explained using Vertex AI's explainable AI tools.

Deployed models can serve online predictions using the API, console UI, or SDK, with the infrastructure to scale for lower latency.

Batch predictions are also supported using undeployed models.

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

Customized notebook instances can be created within the console, equipped with the desired environment and GPUs.

AutoML Edge optimizes models specifically for deployment on edge devices.

Custom Containers allow users to train models built with any framework or language in a Docker container on Vertex AI.

Training can be accelerated with GPUs and enhanced with hyperparameter tuning.

Models can be imported into Vertex AI if they were trained outside of Google Cloud, for serving online and batch predictions.

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

Batch Predictions tab facilitates making predictions on batches of data stored in Cloud Storage.

The overview showcases Vertex AI's comprehensive support for the entire machine learning workflow, from data management to making predictions.

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