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Creating Value With the Breadth and Depth of AI Platform
Watch Craig Wiley, Director of Product Management – Google Cloud, as he breaks down and simplifies AI for enterprises and the adoption of AI.
“As I think about AI, fundamentally AI only does two things. One it helps you grow your market, increase subscribership, increase users, increase their spend or increase their conversion. Or it helps you in the back-end. It can drive efficiencies, reduce costs and drive out waste from the system.
He also talks about how customers have unlocked the power of data by utilizing Google’s AI Platform. From APIs to AutoML to writing your own model code, he will show real-world examples of how customers create value, and critical tips on how to accelerate your own AI journey.
Finally, he will show how can Google Cloud maps business strategy to the right AI absorption strategy and the different ways that Google Cloud can help you deploy AI without compromising flexibility speed, quality or scale.
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44:51 Minutes
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The Strange Phenomenon AI Revealed at Ride-Hailing Company Go-Jek
Go-Jek, Indonesia’s first billion-dollar startup, has seen an incredible amount of growth in both users and data over the past two years. Many of the ride-hailing company’s services are backed by machine learning models hosted on Google Cloud Platform. Models range from driver allocation, to dynamic surge pricing, to food recommendation, and process millions of bookings every day, leading to substantial increases in revenue and customer retention.
By embracing Google Cloud, Go-Jek has overcome many of the technical challenges brought on by its rapid growth. BigQuery has become the cornerstone of their data foundation, scaling seamlessly to meet their immense data storage and processing needs.
Using Pub/Sub as an event stream and Dataflow for unified batch and stream processing has prevented inconsistencies in production data, while simultaneously reducing costs through intelligent resource allocation.
Together, these technologies allow Go-Jek to react immediately to real world events, whether by retraining models with ML Engine, or refreshing data in a low latency data store like BigTable.
Find out how Go-Jek leverages Google Cloud and other lessons they have learned scaling machine learning.
How Cleartrip.com is leveraging Google Cloud to survive the slump in the travel industry
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5:30 Minutes
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With the novel coronavirus COVID-19 sweeping across continents and fatalities climbing every day, it was only a matter of time before countries closed their borders to contain its spread.
In the wake of this decision, travel and tourism, the linchpins of many economies, were among the worst affected.
According to the United Nations World Tourism Organization (UNWTO), the COVID-19 pandemic caused a 22 percent fall in international tourist arrivals during the first quarter of 2020, and could see an annual decline of between 60 percent and 80 percent when compared with 2019.
The impact on the economy and to livelihoods that are dependent on tourism and hospitality has been significant. Prior to the pandemic, the outlook was quite different. Research by UNWTO in 2018 estimated that India would have 50 million outbound tourists by 2020.
Technology was also set to play a huge part in that growth. A Google Travel study showed that 74 percent of travellers were planning their trips on the Internet, and technologies like AI, IoT and VR were all set to be key trends this year.
Now, as borders slowly reopen and travel restrictions are gradually lifted, technology could once again be the game-changer. Manoj Sharma CTO, Cleartrip.com spoke to YourStory about the industry’s road to recovery and how technology will aid that journey.
Read the Full Story on YourStory
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To design and implement a successful machine learning (ML) project, you often need to collaborate with multiple teams, including those in business, sales, research, and engineering. Understanding the essentials of gathering and preparing your data is crucial to align teams, and getting a project off the ground.
Building an ML model that achieves your business’ goal, requires the kind of data that a model can learn from. If the goal is to predict a target output based on some input, you’ll need some data consisting of past input-output examples. For example, to detect (that is, to predict) a fraudulent credit card transaction (the target of the prediction), each example must contain information on a past credit card transaction and whether that transaction was fraudulent or not.
So what are the best practices in preparing and curating data for a machine learning initiative? The answer to this question lies within the guide. You’ll learn:
- To evaluate whether your data is suitable for ML
- What types of data you need–and which ones to eliminate
- How to prevent data leakage
- Tricks to speed up an ML project such as Google’s human labeling service, and Cloud Dataprep
- The importance of the granularity of data
How Domino’s Increased Monthly Revenue By 6% with Google’s Analytical Tools
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Pizza purveyor Domino’s is dominating delivery sales around the world. Today, Domino’s is the most popular pizza delivery chain operating in the U.K., the Republic of Ireland, Germany, and Switzerland—and sales just keep growing.
In these regions in 2014, Domino’s sold 76 million pizzas and generated £766.6 million (1.02 billion USD) in revenue — a 14.6% increase from the previous year.
In the U.K. and Ireland, online sales are increasing 30% year over year and currently account for almost 70% of all sales. Notably, 44% of those online sales are now made via mobile devices.
Multi-Device Purchasing Means Fresh Opportunities
Domino’s is a consistent digital innovator. Much of the company’s success stems from early investments in ecommerce and mobile commerce platforms that help people easily purchase pizzas from different devices.
Domino’s sold its first pizza online in 1999. It then launched an iPhone app in 2010, quickly followed by apps for Android and iPad in 2011, and a Windows app in 2012. By late 2014, Domino’s customers could even order pizzas from Xboxes.
The Domino’s marketing team had assembled a variety of tools to measure marketing performance, keeping pace with the company’s rapid innovations. Unfortunately, measuring siloed analytics and channel-focused tools restricted the team’s ability to fully understand all of the different paths to purchase.
Find out how they worked around this challenge with Google Marketing Platform. Download the case study!
Google’s Intelligent Products Essentials Assist Manufacturers in Product Development Journey
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Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities.
However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features.
That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones.
With Intelligent Products Essentials, manufacturers can:
- Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
- Manage and update products over-the-air: Deploy updates to products in the field, gather performance insights and evolve capabilities over time with monetization opportunities.
- Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.
In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:
- Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
- Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
- Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
- AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.
Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.
Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve.
“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”
Customers adopting Intelligent Products Essentials
GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:
- Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
- Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
- Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.
“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.https://www.youtube.com/embed/zaWAJN8aKOw?enablejsapi=1&
Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences.
“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1.
Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.
“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap
Intelligent Product Essentials is available today. To learn more, visit our website.
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