How to manage ML datasets with Vertex AI
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
📊 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.
🖥️ 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
💡Vertex AI
💡Datasets
💡Image Classification
💡Object Detection
💡Data Governance
💡Regression
💡Entity Extraction
💡Sentiment Analysis
💡Action Recognition
💡Data Labeling
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.