Google cloud platform (GCP) automl services are a set of google cloud platform products with a focus on machine learning and automation. They help you to automate several tasks related to machine learning. In this blog post, we’ll talk about google cloud automl services and some common business problems that can be solved using these GCP automl services.
What are some popular Google Cloud Automl services?
Google cloud automl services include some of the following:
Google Cloud Vision can be used to perform tasks related to image recognition like face detection, OCR (optical character recognition), landmark detection, etc. Google’s cloud vision can detect emotions, understand text, and more. The service is available through the AutoML Vision or pre-trained model. The Google cloud vision can be used to identify and categorize many different things in an image, including the object’s placement throughout the picture. AutoML Vision Edge can be used to build and deploy fast, high-accuracy models to classify images or detect objects at the edge, and trigger real-time actions based on local data. There are two different services: Vision API and AutoML vision. Vision API can be used to detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog, while AutoML Vision can be used to automate the training of your own custom machine learning models. AutoML Vision offers a set of pre-trained machine learning models that you can use without much effort or time spent on training the model yourself. AutoML vision uses predefined neural network architectures optimized for your task as well as your data.
GCP AutoML Text is an automl service that is used to analyze the text in relation to finding sentiments, known entities, and topics/categories associated with the text. It provides both a single-label and multi-label classification model that returns a list of categories that apply to the text found in the data. It also provides an entity extraction model that inspects text data for known entities and labels those entities in the text. Additionally, it also provides a sentiment analysis model that inspects text data and identifies the prevailing emotional opinion within it.
GCP Tabular AutoML is used to analyze the content of tabular data. It provides regression and classification models to extract numerical data from the tabular document and classify the document into different categories. In addition, if the data present in tabular form is time-series data, it also provides a forecasting model for predicting a series of numeric values.
GCP AutoML Video can be used to detect objects in videos, detect and track different features of a video like people or points of interest. It is used to analyze video data to classify shots and segments. It comprises of classification model that can be used to analyze the video data and returns a list of categorized shots and segments. It also includes an action recognition model which is used to identify a list of categorized actions with the moments the actions happened. It also provides an object tracking model which can be used to identify a list of shots and segments where these objects were detected.
GCP AutoML Translation can be used to perform tasks such as creating your own, custom translation models so that translation queries return results specific to your domain.
Vertex AI is a unified AI platform for building, deploying, and scaling ML models faster, with pre-trained and custom tooling. It provides MLOps tools that can be used to manage your data and models at scale. Some of the following can be achieved using the Vertex AI platform:
- Data preparation
- Feature engineering
- Hyperparameter tuning
- Model serving
- Model tuning
- Model performance monitoring
- Model management
What are some real-world applications which can be built using Google Cloud AutoML services?
Here are some real-world applications which can be built using GCP AutoML services:
- Document classification: There are various industry use cases of document classification and GCP Text AutoML with OCR technique can be used for the same. Examples include classifying the text in bank documents, checking whether a document contains some specific information or not, classifying different clauses in legal documents such as contracts, etc.
- Sentiment analysis: Real-world examples of sentiment analysis include spotting relevant tweets to a particular topic, finding positive or negative reviews on google maps for different businesses, finding positive or negative reviews on e-commerce sites for a particular product, or detecting the presence of offensive content in social media posts. GCP Text AutoML can be used to perform sentiment analysis.
- Video analysis: This is especially applicable in video surveillance where motion can be detected whether it’s an intruder or a pet. It can also be used to detect and track different actions like searching for the presence of people in security videos, etc., or tracking vehicles/persons in traffic surveillance videos. One of the popular use cases is identifying fraudulent activities in ATM centers. GCP Video AutoML is used for video analysis which includes object detection and person/vehicle tracking models.
- Object detection in images: Finding similar objects in images is a use case where GCP cloud vision API can be used to apply object detection models. It is useful in scenarios such as classifying animals/plants, food products, and different objects in pictures or photos. A popular use-case for object detection in images is in agriculture where crop health can be assessed by detecting the presence of pests/diseases in an image.
- Face detection from images: There are various use cases where face detection is required from images. This can be used in security cameras, social media platforms to identify people either by name or face recognition, etc.. In criminal cases, it can be used to identify the crime scene in photos. GCP AutoML Vision is useful for this task.
- Handwritten text identification from images: Many times, the text needs to be extracted from cheques. GCP AutoML Vision can be used to identify the handwritten text from these images and extract them.
- Text extraction: GCP Text API helps in extracting different sets of data such as phone numbers, URLs, e-mail addresses, etc., which can then be stored or analyzed for further use cases.
- Extracting data from tabular documents such as invoices, remittances: In fields such as account receivables and account payables, there is a tremendous need for extracting tabular data from invoices. This tabular data in invoices represent line items, invoice numbers, and due dates that are used in the management of these accounts. In addition to this, it can also be applied for extracting tabular data from remittances such as bank statements or utility bills. This can speed up the account reconciliation process of extracting tabular data from documents and matching it with the data in the database. GCP Tabular AutoML can be used for extracting tabular data from invoices, utility bills, remittance documents, etc.
Google AutoML Free Tier Usage Limits
You can use AutoML services free if you adhere to the usage limit. Here are the details for usage limit against different AutoML services:
|AutoML Natural Language||
|AutoML Video Intelligence||
Google cloud automl provides a range of services that can be used for solving various business problems. Google’s AutoML service has been designed to automate machine learning tasks such as object detection, sentiment analysis, and text classification. Google offers these GCP automl services at an affordable cost with the benefits that it is quick and easy to use without needing any expertise in data science or deep neural networks programming skills. In case you want to learn more, please reach out to us.