Google Cloud has heavily invested in security and augmented intelligence. The main goal behind creating Google Cloud to reduce the barrier of entry and make artificial intelligence tools accessible to the largest community of researchers, businesses and developers. Cloud AutoML will aid artificial intelligence experts to be more productive, discover new fields and assist less-skilled engineers to create powerful artificial intelligence systems. Basically, AutoML stands for automated machine learning, which helps to build a similar or new model automatically and business can have it in customized form by imparting its own data-set. This study focuses this technology’s effective adopt

Google Cloud has heavily invested in security and augmented intelligence. The main goal behind creating Google Cloud to reduce the barrier of entry and make artificial intelligence tools accessible to the largest community of researchers, businesses and developers. Cloud AutoML will aid artificial intelligence experts to be more productive, discover new fields and assist less-skilled engineers to create powerful artificial intelligence systems. Basically, AutoML stands for automated machine learning, which helps to build a similar or new model automatically and business can have it in customized form by imparting its own data-set. This study focuses this technology’s effective adoption.

What is Augmented Analytics?

To understand “what is augmented analytics”, first let’s understand what problem it solves and why it is important for businesses. For instance, online data may unveil that a company’s revenue has decreased by 10%. After which a business needs to dig deep into questions like, ‘How it will impact the company? Is the advertising channel not working? Is this decline of an industry trend? Or is it because of other reasons? With these insights, a company needs to take actions and place changes into a business context. Not only troubleshooting the issues but artificial intelligence relieves a company’s dependence from data scientists by enabling automation of insight generation in a company with the help of advanced artificial intelligence and machine learning algorithms.

Augmented Analytics is designed in such a way that it automatically penetrates company’s data, rinses it, analyzes it, then insights are turned into action steps for the marketers or executives with no monitoring from technical person. Therefore, augmented analytics make analytics accessible to every SMB owners.

Why Google Plunged into Artificial Intelligence

Google Cloud has heavily invested in security and AI. The main goal behind creating Google Cloud artificial intelligence is to reduce the barrier of entry and make artificial intelligence accessible to the largest community of researchers, businesses and developers. Machine Learning being the subfield of AI, Cloud AutoML will aid artificial intelligence experts to be more productive, evolve new fields in artificial intelligence and assist less-skilled engineers to create powerful artificial intelligence systems. Basically, AutoML stands for automated machine learning, which helps to build a similar or new model automatically and business can have it in customized form by imparting its own data-set.

What is Google’s Cloud AutoML and AutoML Vision

Google’s Cloud AutoML beta allows developers to train high-quality custom ML models with ML expertise coupled with minimum efforts. Cloud ML is a comprehensive suite of ML products that allow developers with confined expertise to train high-quality models that are crucial to their core business needs, by leveraging Google’s best-in-class technology like Neural Architecture technology and transfer learning. Google has main three Cloud Auto ML products:

• AutoML Natural language: This enables the customer to train and make customize machine learning models which classify documents according to the defined labels.
• AutoMl Translation: This enables the customer to make customized translation models, which gives specific response to the translation queries related to that domain.
• AutoML Vision: This enables the customer to train and make customized machine learning models which classify images according to the defined labels.

We will particularly focus on applications of AutoML Vision. The service is prompt and easy for creation of custom ML models, essentially for image recognition. The interface consists drag-and-drop feature which allows easy image upload, manage and train models. Afterwards, these trained models are viable to deploy directly on Google Cloud. Accuracy with Cloud AutoML is demonstrated by easy classification of public datasets taken from CIFAR and ImageNet.

Industry giants have adopted the technology and let’s focus how they are able to reshape their businesses with Google’s Cloud AutoML Vision.

Case Study 1

Urban Outfitters have been seeking new ways to enhance customers’ shopping experience at URBN. Developing and maintaining all-inclusive set of product attributes which offers customers accurate search results, relevant product recommendations and helpful product filters. However, manual creation of product attributes is time-consuming and arduous. To troubleshoot this, the company has been evaluating Cloud AutoML to ease down or automate the product attribution process by identifying nuanced product characteristics like neckline styles and patterns. Cloud AutoML has assured customers with better discovery, search experiences and recommendation.

Case Study 2

ZSL is known international conservation charity which dedicatedly works for conservation of animals and their habitats. The company requires a device technology to track wildlife populations, which facilitate their easy-distribution and understand the impact humans have on these animals. Subsequently, has installed series of camera traps in the jungles which captures or take a snap of passing animals when triggered by motion or heat. These cameras takes millions of images which afterwards is manually annotated and analyzed with the relevant species such as lions, giraffes and elephants, etc., which is an expensive and labor-intensive process. Therefore, ZSL’s dedicated Conservation Technology Unit has keenly tied-up with Google’s Cloud ML team, which will support the development of this exciting technology. ZSL aims to use Cloud ML to automate the tagging of these photos, resulting in cutting costs, allowing wider-scale deployments and achieve better understanding of different conservation ways of world’s wildlife.

Case 3:

Statement: In Japan, Ramen Jiro is well-known chain restaurant franchises for ramen, due to generous portions of noodles, soup and toppings served at very low-prices. They own 41 branches around Tokyo, and their basic menu in all these shops is same. If the customer is new to Ramen, he may got confused in recognizing what shop each bowl is made at due to their similar look.

Data Scientist Kenji wondered if artificial intelligence could resolve this issue. Therefore, he collected 48,244 photos of soup bowls of different locations from internet. Removing less suitable photos he was left with 48,000. He started working upon project with help of AutoML Vision’s alpha version. He successfully trained a model with shop labels and ramen photos, he achieved 94.5% accuracy (F1 score, with 94.5% recall and 94.8% precision) for prediction of shop just from the photos. The model is highly accurate even when each shop used same table design and bowl. Hence, results have optimal accuracy in a very short amount of time.

Considerations:

Cloud Auto ML is capable to adroitly solve an array of challenges for different domains of companies, however, there are few considerations to be alert of. The first challenge is data quality. At many times, Machine Learning enabled devices may have poor results due to biased or poor quality data. The second consideration is, AutoML is still restricted in offering flexibility of problems it can solve. In image recognition, two notable challenges includes distance between certain objects and automatically recognizing where objects lie in an image. The models created by AutoML still needs proper data processes and testing.