Google AutoML

Quick Solution Overview

What Is Google Cloud AutoML?

Google Cloud AutoML is a suite of machine learning (ML) tools that enables developers and data scientists to build and deploy custom ML models with minimal effort and expertise. It is part of the Google Cloud platform and includes a range of pre-trained models and tools for training, evaluating, and deploying custom models.

AutoML is designed to make it easier for organizations to build and deploy ML models by automating many of the tasks involved in the ML process, such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. It also includes tools for building custom ML models using a graphical user interface (GUI) and for integrating ML models into applications using APIs.

AutoML is particularly useful for organizations that want to quickly build and deploy ML models without the need for extensive training or development. It is also useful for organizations that want to experiment with different ML techniques and models to find the best fit for their needs.

What Is AutoML and Why Is It Important?

AutoML is a new approach to artificial intelligence and machine learning (AI/ML). In the past, AI/ML models were characterized as 'black boxes', meaning they were difficult to understand and reverse engineer. These models could achieve high performance with efficient use of resources, but made it difficult to track how the algorithm delivers its output. This made it difficult to predict outcomes and choose the right model for a given problem.

AutoML helps break the black box, making machine learning models more accessible. It automates much of the process of applying algorithms to real-world scenarios—while making it possible for anyone to understand the inner logic of the algorithm and how it makes decisions. AutoML provides information about ML algorithms that would be too time-consuming or resource-intensive for humans to obtain.

AutoML uses the concept of “meta-learning”—automatically fine-tuning the end-to-end machine learning process using machine learning methods.

An Overview of Google Cloud AutoML Solutions

Google Cloud AutoML includes several components that let you create machine learning models in an automated and transparent manner.

Vertex AI

Vertex AI integrates Google’s AutoML and AI Platform into a unified API, client library, and user interface. It offers both AutoML and custom training options, letting you save, deploy, and request predictions via the API.

AutoML Natural Language

AutoML Natural Language is provided as a REST API. The system performs sentiment analysis and helps to classify documents according to various characteristics.

AutoML Natural Language also handles extracting specific entities in a document and labeling them for easy identification. The API also provides options to customize categories, labels, and sentiments based on your needs.

AutoML Translation

AutoML Translation is a set of models that can translate text from one language to another. You can use this platform to create custom language translation models, while simplifying translation queries and responses. AutoML Translation supports up to 50 language pairs.

Cloud Video Intelligence

Cloud AutoML's Video Intelligence interface can annotate videos with custom tags. It analyzes video to enable content detection in applications. The platform analyzes streaming video to detect shot changes and track specific objects. Real-time analysis of video data sets helps derive insights that help improve customer experience, including content recommendations.

Cloud AutoML Vision

AutoML Vision supports cloud and edge computing to get insights from specific images. You can leverage pre-trained AutoML Vision models to analyze images and extract insights. You can also use the Vision API to classify images based on labels and custom labels. AutoML Vision builds image metadata using object and face detection, handwriting recognition, and more. The program provides many pre-trained ML models for deployment using REST and RPC APIs.

Cloud AutoML Tables

The Cloud AutoML Tables interface allows teams to very quickly and efficiently create custom machine learning capabilities for analysis of tabular data. The AutoML Tables interface allows you to generate automated ML code, making it easier to deploy ML features.

The AutoML Tables interface lets you train ML models by using example tabular data, training an ML model to make predictions on a specific data set. It gathers data, prepares it, ingests tabular data for training predictive ML models, and checks the model’s metrics to test its accuracy. It is then possible to create a tested model that can be used in production.

Learn more in our detailed guide to AutoML solutions

How Does Google Cloud AutoML Work?

Here's an overview of how Google’s AutoML solution works:

  • Data acquisition: AutoML includes tools for acquiring and preparing data, including tools for accessing and importing data from external sources for cleaning and preprocessing.
  • Data preprocessing: After acquiring your data, you will need to preprocess it to ensure it is in a suitable format for modeling. This may involve tasks such as normalizing and scaling the data, filling in missing values, and encoding categorical variables. AutoML includes tools for normalizing and scaling data, filling in missing values, and encoding categorical variables.
  • Data engineering: Data engineering involves preparing the data for modeling by converting ordinal or categorical data into numerical vectors, extracting features, and selecting the most relevant and predictive features for the model. This may involve tasks such as feature selection, feature extraction, and feature engineering. AutoML includes tools for selecting and extracting relevant features from the data.
  • Data modeling: After acquiring and preprocessing your data, you will need to build an ML model that can learn from the data and make predictions. AutoML includes a range of pre-trained models that you can use as-is or fine-tune to your specific needs. It also includes tools for building custom ML models using a graphical user interface (GUI).
  • Hyperparameter tuning: To get the best performance from your ML model, you will need to tune its hyperparameters, which are the settings that control the behavior of the model. AutoML includes tools for hyperparameter tuning, which can help you find the optimal hyperparameter settings for your model.
  • Prediction: Once you have trained your ML model, you can use it to make predictions on new data. AutoML includes tools for evaluating the performance of your model and for deploying it as a web service or API, so you can easily integrate it into your applications or systems.

Getting Started with AutoML and Vertex AI

Google AutoML includes Vertex AI, which can perform supervised learning tasks. The details of the algorithm used and the training method depend on the data type and use case.

Here is a general process for working with Vertex AI:

  1. Collect data—determine the data you need to train and test your model based on the results you want to achieve.
  2. Prepare your data—make sure your data is formatted and labeled correctly.
  3. Training—set the parameters and build the model.
  4. Evaluate—review model metrics.
  5. Deploy—prepare the model for production use.

Stay Ahead of the ML Curve with Run:ai

In today’s highly competitive economy, enterprises are looking to machine learning and deep learning (ML/DL) to transform big data into actionable insights. ML/DL can help organizations better address their target audiences, improve their decision-making processes, and streamline their supply chains and production processes. However, to fully operationalize ML/DL, organizations must embrace machine learning operations (MLOps).

Run:ai’s AI/ML virtualization platform is an important enabler for MLOps processes. Focusing on deep learning neural network models that are particularly compute-intensive, Run:ai creates a pool of shared GPU and other compute resources that are provisioned dynamically to meet the needs of jobs in process.

By abstracting workloads from the underlying infrastructure, organizations can embrace MLOps and allow data scientists to focus on models, while letting IT teams gain control and real-time visibility of compute resources across multiple sites, both on-premises and in the cloud.

See for yourself how Run:ai can operationalize your data science projects, accelerating their journey from research to production.