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AI

Device42 launches AI recommendation engine for cloud usage

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Device42, a cloud discovery platform, this month launched a multicloud migration and recommendation engine the company claims is the first to support all major cloud providers. Using machine learning to drive its suggestions, Device42 says the service can perform real-time discovery of IT resources to create an inventory, leveraging dependency mapping to show the relationship and impact of resources on business units.

Organizations often face risks of business outages and disruptions when attempting to migrate to the cloud. And according to IDG research, only 25% achieve their initial goals. Additional reporting by Unisys has found that more than one-third of businesses fail to capture “notable benefits” from their cloud computing projects.

Device42’s new recommendation engine aims to help with cloud migration via AI-driven analysis. It works by first performing a discovery of all resources and apps, creating a directory. Once the inventory finishes, the engine delivers a cost analysis to recommend which apps to move to the cloud and which cloud — Amazon Web Services (AWS) or VMWare on AWS, Microsoft Azure, GCP, or Oracle — might be best for each app.

“We know migration is a big challenge for many organizations, and we’ve heard it loud and clear from our customers. We built this engine to help our customers automate the processes and help them reduce risk,” Device42 founder and CEO Raj Jalan said in a statement.

‘Right-sizing’ cloud deployments

According to RightScale, in 2017 26% of enterprises with more than 1,000 employees spent over $6 million a year in the public cloud. But it’s estimated that a fair amount of that enterprise cloud spend is going to waste. The same report found that the average waste in cloud costs was 35%, netting out to $10 billion each year across AWS, Azure, and GCP.

Device42’s engine can provide data about the cost of resources and their performance impact, as well guidelines to support best practices. It helps determine the most efficient course of action, including whether to re-architect apps, and works to identify the right sizes for cloud instances.

According to Jalan, the engine matches operating systems from on-premises solutions to the cloud so apps function after migration. Savings come from reservation purchase options and algorithms that factor in networking and storage costs, along with CPU and memory.

“The [engine] provides the visibility and information users need to make key decisions across cloud instances,” Jalan continued.

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Categories
AI

How Intel and Burger King built an order recommendation system that preserves customer privacy

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The pandemic has placed enormous strains on the restaurant and fast food industries. Within a month of the health crisis, 3% of restaurants had closed for good and another 11% anticipated doing so within the following month, according to a National Restaurant Association study. While fine-dining and casual dining establishments suffered the bulk of the impact, the fast food industry wasn’t immune. A Datassential survey found that sales among fast food operators declined 42% during the first few weeks of the pandemic.

As more customers began relying on take-out and drive-thru options versus indoor dining, fast food retailers like Burger King turned to AI and machine learning for solutions. In collaboration with Intel, Burger King developed an AI system that recommends items on touchscreen menu boards to customers as they’re about to order. It can predict whether a customer will order a hot or cold drink or a light or large meal, potentially saving time and leading to a better customer experience.

Burger King and Intel say the solution has already been piloted in over 1,000 Burger King locations. 

Burger King isn’t the first fast food chain to experiment with AI in customer service. McDonald’s has been using AI in its drive-thrus since acquiring tech company Dynamic Yield in 2019. Dunkin’ Donuts is testing drive-thrus that can recognize a loyalty member as soon as they pull up. Some Sonic drive-ins recently got AI-powered menu kiosks. And Chick-fil-A is using AI to spot signs of foodborne illness from social media posts.

As Luyang Wang, director of advanced analytics and machine learning at Burger King, explained to VentureBeat via email, fast food recommendation has its own set of unique challenges. There’s no easy way to identify customers and retrieve their profiles because all of the recommendations happen offline. Moreover, context features like location, time, and weather conditions have to be preprocessed before they can be loaded into a model.

To solve these challenges, TxT was built with what’s called a “double” Transformer architecture that learns real-time order sequence data, as well as features like location, weather, and order behavior. TxT leverages all data points available in a restaurant without having to identify customers prior to the order-taking process. For example, if a customer puts a milkshake as the first item in their basket, that will influence what TxT suggests — based on what’s been sold in the past, what’s selling today, and what is sold at that location.

TxT was developed within Analytics Zoo, Intel’s open source platform for big data analytics workloads running in datacenters. Intel and Burger King collaborated to create an end-to-end recommendation pipeline, which includes distributed Apache Spark data processing and Apache MXNet training on an Intel Xeon cluster. The TxT model was deployed using Intel’s RayOnSpark library, which allows enterprises to directly run programs on existing clusters.

According to Wang, TxT has already led to surprising sales insights. For one, Burger King customers will order milkshakes in any weather — even when it’s cold out. And people are much more willing to add a dessert when they have a high-calorie basket versus a low-calorie basket.

“At Burger King, we are always looking to improve our guests’ experience,” Wang said. “This AI recommender system — Transformer Cross Transformer (TxT) — allows Burger King to better learn customer habits and, essentially, better communicate with guests.”

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Repost: Original Source and Author Link