How to manage ML datasets with Vertex AI

Google Cloud Tech
23 May 202107:39

TLDRThis video introduces Vertex AI, a comprehensive tool for managing machine learning datasets. It covers the importance of datasets in the ML lifecycle, the ability to use pre-trained APIs for generic use cases, and the process of creating custom datasets for model training. The video explains the supported data types, including image, tabular, text, and video, and their applications like classification, object detection, and sentiment analysis. It also provides tips for ensuring training data quality and concludes with a brief guide on managing datasets in the Vertex AI console.

Takeaways

  • 🚀 Vertex AI provides comprehensive tools for every step of the machine learning workflow, from data management to model deployment and predictions.
  • 📊 For generic use cases, one can leverage pre-trained machine learning APIs without needing to manage custom datasets.
  • 💾 Datasets in Vertex AI are central repositories that make data discoverable, annotatable, and trackable for governance and model comparison.
  • 🖼️ Image datasets support various tasks like classification, object detection, and segmentation, with tips to ensure training images match user input for production.
  • 📈 Tabular datasets are utilized for regression and classification tasks, with support for hundreds of columns and millions of rows.
  • 📝 Text datasets can be used for classification, entity extraction, and sentiment analysis, assigning labels to documents or identifying specific text entities.
  • 🎥 Video datasets support classification of entire shots and frames, action recognition, and object tracking with bounding boxes and timestamps.
  • 🔗 The console interface of Vertex AI allows for easy creation and management of different types of datasets, with direct access to data analysis tools.
  • 🏗️ When creating datasets, one can import files directly, use a CSV for labeled data, or even employ Vertex AI's data labeling service for human-assisted labeling.
  • 📊 Analysis of datasets within the console provides insights such as the number of images per label or rows and columns in tabular data.
  • 🔜 The video script provides a high-level overview, with the next video in the series diving into building and training machine learning models in Vertex AI.

Q & A

  • What is the main focus of the video?

    -The main focus of the video is to explain how to manage machine learning datasets with Vertex AI, which is a suite of tools for various steps in the machine learning workflow.

  • What are the advantages of using Vertex AI for datasets?

    -Vertex AI provides a centralized place to discover data, allows for data annotation and labeling, tracks lineage for data governance, and enables comparison of model metrics.

  • What are the four supported data types in Vertex AI?

    -The four supported data types are image, tabular, text, and video.

  • How does image classification work in Vertex AI?

    -Image classification involves models predicting one or many labels from an image, such as identifying types of dog treats from images.

  • What is the recommended number of images per label for good model performance?

    -It is recommended to include at least 1,000 images per label, but you can start with 10 per label.

  • What types of tasks does tabular data support in Vertex AI?

    -Tabular data supports regression (predicting numerical values) and classification (predicting categories associated with examples), as well as forecasting (predicting likelihood of events or demands).

  • How can text datasets be utilized in Vertex AI?

    -Text datasets can be used for classification (assigning labels to entire documents) and entity extraction (identifying custom text entities within a document), as well as sentiment analysis.

  • What are some functionalities provided for video datasets in Vertex AI?

    -Video datasets support classification for labeled predictions on entire video shots and frames, action recognition (identifying specific actions in clips), and object tracking (labeling, bounding boxes, and timestamps for tracked objects).

  • How can users import their data into Vertex AI?

    -Users can import data directly from their computer, from Cloud Storage, or select a table from BigQuery directly for tabular data. For images, they can upload files or import a CSV with image URLs and labels.

  • What is the purpose of the Data Guide in Vertex AI?

    -The Data Guide provides requirements and recommendations for preparing and uploading data for machine learning tasks in Vertex AI.

  • How can users analyze their datasets in Vertex AI?

    -Once the data is uploaded, users can analyze various properties such as the number of images per label, rows, and columns for tabular data, or labels for text data.

Outlines

00:00

📊 Introduction to AI Simplified and Vertex AI

The video begins with Priyanka introducing the concept of AI Simplified, a platform designed to make data useful. The focus is on a hypothetical company with vast amounts of data looking to leverage it for meaningful predictions and business growth. The solution proposed is Vertex AI, a comprehensive suite of tools that facilitates every step of the machine learning workflow, from data management to model training, evaluation, deployment, and prediction. The video aims to explore the first step in the machine learning lifecycle: data sets. It emphasizes the ease of using pre-trained machine learning APIs for generic use cases and the importance of creating custom machine learning models with a robust collection of data. The video outlines the process of creating data sets, including data upload, modification, and training model initiation. It also discusses the four supported data types (image, tabular, text, and video) and their respective applications like image classification, object detection, segmentation, regression, classification, entity extraction, sentiment analysis, and video-related tasks. The importance of diverse and representative data for model performance is stressed, with recommendations for the quantity and variety of data included.

05:00

🖥️ Creating and Managing Data Sets in the Vertex AI Console

The second paragraph shifts focus to the practical aspect of creating and managing data sets within the Vertex AI console. It guides viewers through the process of navigating to Vertex AI, selecting the data set section, and choosing the appropriate data type for their objectives. The paragraph provides a walkthrough for creating an image data set, starting from checking the Data Guide for requirements and recommendations, to importing files directly or using a CSV file with labels. It also touches on the option of using data labeling services for unlabeled data. After uploading, the video explains how to create and assign labels to images, analyze the data set properties, and ensure the quality of the data. The process for managing tabular and text data sets is similarly outlined, highlighting the ability to upload files, analyze data, and add labels. The video concludes with a brief overview of managing video data sets and ends with an encouragement for continued discussion in the comments section, inviting viewers to share their ML use cases and data set experiences.

Mindmap

Keywords

💡Machine Learning

Machine Learning is a subset of Artificial Intelligence that focuses on the development of computer programs that can access data and learn from it. In the context of the video, it refers to the process of training models to make meaningful predictions and decisions without explicit programming. The main theme revolves around utilizing Vertex AI to manage datasets and train custom machine learning models for various business applications.

💡Vertex AI

Vertex AI is a platform by Google Cloud that provides tools for every step of the machine learning workflow. In the video, it is highlighted as a solution that helps companies manage datasets, train models, evaluate, deploy, and make predictions. It simplifies the machine learning process by offering pre-trained APIs and supporting the creation of custom models, catering to users with varying levels of machine learning expertise.

💡Datasets

Datasets are collections of data used to train machine learning models. They are crucial for making data discoverable and annotatable from a central place. In the video, datasets are discussed as the first step in the machine learning lifecycle, emphasizing their importance in creating custom models. The platform supports various data types, such as image, tabular, text, and video datasets, each with specific applications like classification, object detection, and sentiment analysis.

💡Image Classification

Image Classification is a machine learning technique where models predict one or many labels from an image. It is one of the applications of image datasets mentioned in the video. For instance, identifying different types of dog treats from images is an example of image classification. The process involves training the model to recognize and categorize visual features, which is essential for tasks like object recognition and image organization.

💡Object Detection

Object Detection is the process by which a model identifies and locates items within an image by drawing bounding boxes around them. As illustrated in the video, an example of object detection is identifying the location of different vegetables in a salad picture. This technique is vital for applications like autonomous vehicles, security systems, and image-based inventory management.

💡Data Governance

Data Governance refers to the set of policies, procedures, and standards that ensure the proper management, usage, and quality of data within an organization. In the video, it is mentioned in the context of tracking data lineage within datasets. Good data governance practices are crucial for maintaining data integrity, ensuring compliance with regulations, and building trust in the data-driven decision-making process.

💡Regression

Regression is a statistical method used in machine learning to predict a numerical value based on the relationship between variables. In the context of the video, tabular datasets support regression, where the model predicts a numerical outcome, such as sales forecasting over a period of time. It helps businesses understand trends and make informed decisions based on historical data and potential market behaviors.

💡Entity Extraction

Entity Extraction is a natural language processing technique that involves identifying and classifying specific pieces of information, known as entities, within a text document. In the video, it is used as an example of a text dataset application, where the model identifies custom text entities, such as pricing phrases, in customer support emails. This technique is essential for information retrieval, content analysis, and data summarization.

💡Sentiment Analysis

Sentiment Analysis is the process of determining the emotional tone behind a block of text, which can be used to gain insights into customer opinions and attitudes. As discussed in the video, it is a text dataset application that helps understand if a customer is happy, upset, or frustrated. Sentiment analysis is widely used in social media monitoring, product reviews analysis, and customer feedback processing to improve customer experience and satisfaction.

💡Action Recognition

Action Recognition is a video analysis technique that involves identifying specific actions or events within a video sequence. In the video, it is mentioned as a capability of video datasets, where the model recognizes clips in a video where specific actions occur, such as a dog making a touchdown in a football game. This technology has applications in surveillance, sports analytics, and automated video content moderation.

💡Data Labeling

Data Labeling is the process of assigning descriptive tags or labels to data, which helps in training machine learning models. As highlighted in the video, it is an essential step in creating datasets, where users can add corresponding labels to their images or import a CSV file with pre-labeled data. Accurate data labeling is crucial for training models to correctly interpret and respond to input data, ensuring the model's performance in real-world scenarios.

Highlights

Managing ML datasets with Vertex AI streamlines the machine learning workflow for businesses.

Vertex AI offers tools for every step of the ML lifecycle, from data management to model deployment and predictions.

Custom machine learning models require a collection of data for training, which is facilitated by datasets.

Datasets make data discoverable from a central place and enable annotation and labeling within the UI.

Data lineage tracking is supported for governance, and model metrics can be compared between different models.

Creating a dataset involves uploading and importing data, followed by modifications and model training.

Four data types are supported: image, tabular, text, and video datasets.

Image datasets support tasks like classification, object detection, and segmentation for improved model performance.

For image datasets, it's crucial to include diverse examples to minimize training-serving skew.

Tabular datasets support regression and classification, including forecasting for predicting numerical values and likelihoods.

Text datasets can be used for classification, entity extraction, and sentiment analysis.

Video datasets support classification of entire shots and frames, as well as action recognition and object tracking.

The console interface of Vertex AI allows for easy creation and management of various types of datasets.

When creating image datasets, it's recommended to check the Data Guide for requirements and best practices.

For labeled data, a CSV with image URLs and labels can be imported; for unlabeled data, the data labeling service can be utilized.

Analyzing datasets in the console provides insights like the number of images per label and data properties.

The next steps in the ML workflow, including building and training models, will be explored in upcoming videos.

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