Google Cloud AutoML for Machine Learning Solutions: Automation Exemplified

Sanam Malhotra | 25th January 2020

Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based machine learning development services are overcoming the business challenges of processing heavy data volumes. With accuracy and efficiency, cloud technologies like Google cloud AutoML are encouraging the development of dynamic machine learning solutions. At Oodles, we are testing the model performance of AutoML Natural Language to build domain-specific solutions for global businesses. Let’s explore how cloud AutoML for machine learning solutions is triggering automation beyond human intelligence. 


How does Google AutoML Natural Language work?

AutoML Natural Language runs on large volumes of data and algorithms. The cloud service enables businesses to perform supervised machine learning by training a system to identify patterns from labeled data. With AutoML Natural Language service, businesses can easily build custom and cloud-based machine learning solutions for specific tasks like content recognition from textual data. On a deeper level, machine learning in AutoML Natural Language involves three major steps-

1) Data Preparation

The first step to build a domain-specific model is to organize data in the form of inputs and answers. While inputs include labeled examples of certain text that a business wants to classify, answers include categories to be predicted by the ML model. A bare minimum of 1000 examples is essential to train the model efficiently.

2) Evaluation

After training AutoML Natural Language with rich and goal-oriented data, it is time to evaluate its performance. The main idea is to assess what model’s output on test examples with techniques such as score threshold, true positives and negatives, precision, and recall.


cloud automl machine learning development services

AutoML uses Rest API to generate predictions by analyzing a wide array of data structures over the cloud.

3) Model Testing

AutoML has this unique ability to reserve and use 10% of every dataset for testing purposes automatically. However, developers can also perform manual sanity checks by feeding text examples into the text box of the Predict page. Testing with variable example texts enables developers to check what label does the model chooses and why.


AutoML Natural Language Use Cases

a) AutoML Text Classification

b) Sentiment Analysis

c) Entity Extraction

Sorting and processing an explosion of unstructured textual information is a major challenge for digital businesses. With AutoML, businesses can categorize complex digital content such as blogs, articles, news stories, social media posts, etc. AutoML Natural Language supports both native and scanned PDFs in the English language with a training capacity of up to 1 million documents. From eCommerce to healthcare, businesses can use AutoML’s text classification in the following ways-

a) Classify and translate real-time eCommerce customer query data into definite product names to optimize the supply chain.

b) Gauge the sentiment and emotions of users across social media tweets to track and evaluate user feedback on certain business services.

c) Categorizing candidate qualifications from resumes and CVs to save manual labor in sorting and classifying candidate capabilities.

Also read- Expansive Applications of Text Analytics using Amazon Comprehend


Difference between Google AutoML Natural Language and Natural Language API

Google Cloud AutoML is a gateway for businesses seeking to build personalized machine learning solutions than the standard Natural Language API. Customization is the key differentiator between Google AutoML Natural Language and standard Natural Language API. While Natural Language API proves effective for chatbot development services, AutoML is more essential for driving insights and predictions from textual and user data.

Also read- Why Scikit-learn is Optimum for Python-based Machine Learning


Deploying Cloud AutoML for Machine Learning Solutions with Oodles AI

As we leap into the new year 2020, big data analytics powered by machine learning technologies are poised to generate greater value for businesses. We, at Oodles, are combining our forces with emerging cloud-based technologies to develop business-oriented machine learning solutions.

Our machine learning development services are effective at handling prodigious volumes of business and consumer data to extract actionable insights and predictions. Our team has developed various cloud-based machine learning systems including an automated Diabetic Prediction System that identifies diabetes in patients with over 90% accuracy. In addition, our AI team has experiential knowledge in IBM Watson, Azure, and AWS Cloud consulting services.

To learn more about our artificial intelligence and machine learning services, reach out to our AI development team.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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