Seit Jahren ist bekannt, dass in Apples HomePod mini ein Temperatur- und Luftfeuchtigkeitsmesser steckt. Der soll nun per Update aktiviert werden. (Homepod, Apple)
Quelle: Golem
Ubitricity hat von den geplanten 1.000 Straßenlaternen in Berlin erst 58 zu Ladesäulen aufgerüstet. (Ladesäule, Auto)
Quelle: Golem
Xpeng senkt die Preise für seine E-Autos. Dieser Schritt erfolgt nach Preisanpassungen Teslas. (Xpeng, Elektroauto)
Quelle: Golem
Nach der Abschaltung von Googles Stadia kann der Controller auf Bluetooth umgeschaltet werden. Wir zeigen das Verfahren Schritt für Schritt. (Stadia, Google)
Quelle: Golem
Paketlieferungen mit Amazon Prime dauern länger und Prime Video zeigt mehr Werbung – vorerst testet Amazon das Prime-Lite-Abo nur im Ausland. (Amazon Prime, Amazon)
Quelle: Golem
Apple soll neben seinem Mixed-Reality-Headset ein zweites, reines AR-Gerät geplant haben – dieser Plan hat sich nun geändert. (Apple, Augmented Reality)
Quelle: Golem
Dialogflow CX is a powerful tool in Google Cloud that you can use to design conversational agents powered by Natural Language Understanding (NLU) to transform user requests into actionable data. You can integrate voice and/or chat agents in your app, website, or customer support systems to determine user intent and interact with users.If you’ve ever wanted to get started with Dialogflow CX, you might have seen or ran through the quickstart steps to build a shirt ordering agent that you can ask for the store location, get store hours, or make a shirt order.While going through the quickstart steps, you might find yourself wanting to codify all of the Dialogflow CX components and settings, which would help you quickly spin up agents and manage their configuration programmatically. In fact, you might already be using infrastructure as code tooling and best practices to manage virtual machines in Compute Engine, Kubernetes clusters in GKE, or topics and subscriptions in Pub/Sub. You can also use the same infrastructure as code approach with your Dialogflow CX agents: Terraform and Google Cloud to the rescue!You can use the Terraform modules for Dialogflow CX along with the sample Terraform + Dialogflow CX configuration files to reproduce the chatbot/agent described in the “build a shirt ordering agent” quickstart. Try them out and spin up a Dialogflow CX agent with a single command in your own Google Cloud account!SetupThere are a few things that you’ll need to set up before you run the sample Terraform configuration files for Dialogflow CX.Register for a Google Cloud account.Enable the Dialogflow CX API.Install and initialize the Google Cloud CLI.Install Terraform.UsageOnce you’ve completed the setup on your local machine, you’re ready to spin up your own fully-configured Dialogflow CX agent in seconds:Clone the CCAI samples repository and cd into the dialogflow-cx/shirt-order-agent/ directory.Edit the values in variables.tf to specify your Google Cloud project ID along with your desired region and zone.Run terraform init to initialize the directory that contains the Terraform configuration files.Run terraform apply, the command that spins everything up!Once you run terraform apply and confirm the proposed plan, you’ll see messages about all of the components that were provisioned, including the agent, pages, intents, flows, and more:code_block[StructValue([(u’code’, u’google_dialogflow_cx_agent.agent: Creating…rngoogle_dialogflow_cx_agent.agent: Creation complete after 2srngoogle_dialogflow_cx_entity_type.size: Creating…rngoogle_dialogflow_cx_page.store_location: Creating…rngoogle_dialogflow_cx_intent.store_hours: Creating…rngoogle_dialogflow_cx_page.store_hours: Creating…rngoogle_dialogflow_cx_page.order_confirmation: Creating…rngoogle_dialogflow_cx_intent.store_location: Creating…rngoogle_dialogflow_cx_intent.store_hours: Creation complete after 1srngoogle_dialogflow_cx_page.store_location: Creation complete after 1srngoogle_dialogflow_cx_page.order_confirmation: Creation complete after 1srngoogle_dialogflow_cx_page.store_hours: Creation complete after 1srngoogle_dialogflow_cx_intent.store_location: Creation complete after 1srngoogle_dialogflow_cx_entity_type.size: Creation complete after 1srngoogle_dialogflow_cx_page.new_order: Creating…rngoogle_dialogflow_cx_intent.order_new: Creating…rngoogle_dialogflow_cx_intent.order_new: Creation complete after 0srngoogle_dialogflow_cx_page.new_order: Creation complete after 0s’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ef4c6f145d0>)])]Now that you’ve provisioned your agent in Dialogflow CX, you’re ready to view and test your agent in the Dialogflow CX Console!How it worksWe’re using the Terraform modules for Dialogflow CX to define a conversational agent and all of its components. We’ve reproduced the agent described in the build a shirt ordering agent quickstart.All of the agent’s associated entity types, flows, intents, and pages are created and managed with Terraform, so you can edit your Terraform configuration files to change certain parameters, run terraform apply, and see your changes instantly reflected in the Dialogflow CX console.You might notice that the flows.tf file actually uses a local-exec command within a null_resource block to make a REST API call instead of using a Terraform resource for Dialogflow CX to define the flow. This approach was used since Dialogflow CX creates a default start flow when the agent is created rather than being created and managed by Terraform. As a result, we can use a REST API call to PATCH the default start flow and then modify its messages and routes. We can still use Terraform to templatize and trigger the REST API command, which means that you can manage any setting that is also available in the Dialogflow CX REST API, or even add custom callbacks to other Google Cloud services if needed.SummaryIt’s convenient to be able to manage conversational agents as code using Terraform in Google Cloud. We get all of the benefits of Dialogflow CX with the convenience of Terraform to manage everything in a stateful and version-control friendly way.Now that you’ve captured all of your Dialogflow CX agent settings and configuration in Terraform, you are ready to check your Terraform scripts into version control, spin up and destroy agents as you please using terraform apply and terraform destroy, or even store remote Terraform state in Google Cloud using the GCS backend.Take a look at the Terraform + Dialogflow CX sample code along with the Terraform modules for Dialogflow CX so you can spin up your own Dialogflow CX agents with a single command. If you found this Terraform code sample useful, be sure to star, watch, or ask questions in our CCAI samples repository on GitHub!aside_block[StructValue([(u’title’, u’Terraform + Dialogflow CX sample code’), (u’body’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ef4c5a19750>), (u’btn_text’, u’TRY IT OUT!’), (u’href’, u’https://github.com/GoogleCloudPlatform/contact-center-ai-samples/tree/main/dialogflow-cx/shirt-order-agent’), (u’image’, None)])]
Quelle: Google Cloud Platform
The new year brings opportunity for thoughtful reflection about the past year, both personally and professionally. 2022 was a year of firsts for me—first time having clam chowder at Pike Place Market as a local, first time going shopping for heels with my daughter, and first time delivering an Azure keynote at Inspire as a Microsoft employee when pre-COVID-19, I was a Partner listening in the audience. And here is another first; the start of a new blog series where I plan to share more about noteworthy and inspiring data and AI innovations we are releasing across Microsoft. Given the National Retail Federation’s Big Show this week, I’ll also highlight how these innovations impact retail.
Let’s explore what’s new for Azure Data & AI this month:
Microsoft underscores resilient retail at NRF
A bellwether for economic and societal trends, the retail industry continues to be on the front line of adaptive innovation. And rather than trying to predict the future, retailers are working to achieve greater business agility necessary to thrive in it. This means that, like Majid Al Futtaim Retail, they automate tedious processes so employees can focus on higher-value tasks. Like Grupo Bimbo, they unify disparate data points in real time so that employees can access a central source of truth when and where they need it. And, like CCC Group, they stay laser-focused on delivering differentiated customer experiences to build loyal fans. Across each of these organizations, data is seen as an accelerant for growth, powering more personalized customer experiences, cost-efficient supply chains, and proactive responses to market trends.
Business agility requires people, processes, and technologies to work in harmony, and to align on how massive amounts of data are managed, analyzed, and actioned to respond to market demands. Increasingly, these efforts focus on driving sustainable growth that limits carbon emissions, for example by using machine learning to more accurately forecast demand to reduce excess inventory and waste.
We know cloud technologies like databases, containers, and AI can enable more accurate decisions, but it can be challenging to ensure these technologies speak to each other in the right way at the right time on a global scale. This is where Azure and the Microsoft Intelligent Data Platform—the description we use to refer to all of the Data & AI Azure products and services we offer shine. With managed databases like Cosmos DB, analytics services like Azure Synapse, and leading AI offerings, retailers can take advantage of the “by design” integration the Microsoft Intelligent Data Platform offers, which means they are able to invest more time in creating value rather than integrating and managing their data estate.
The Microsoft Intelligent Data Platform came to life through new immersive demo experiences this year at NRF and I’m going to briefly highlight what we showcased at the conference. Earlier this week at NRF, Microsoft’s Alysa Taylor and Shelley Bransten spoke on the topic of Resilient Retail and shared examples of organizations digitizing their businesses to do more with less. You can read more about their talks on our Industry blog.
Microsoft also met with customers at our NRF booth to discuss strategies for making sense of all their data. For example, these two demos highlight how tight integrations between data, analytics, and AI can help make resilient retail a reality.
Wide World Importers (WWI), a global supermarket chain, wants to maximize the value of their data estate. By using Azure Synapse pipelines with Cosmos DB, they get real time insights which are automatically shared with the right decision makers through tools like Azure Data Explorer and Power BI. They’re able to track their supply chain data in real time and use predictive AI to reduce costs. They’re also able to govern their data estate from a single application one pane of glass using Microsoft Purview.
Next, Wide World Importers taps into the power of Azure AI to build a more connected customer experience. They use Azure Form Recognizer to detect and redeem promotional offers. Azure Cognitive Search helps customers find product information more quickly by recognizing their search intent and helps WWI deliver more personalized recommendations. Pre-built AI capabilities, such as speech recognition and computer vision, also differentiate the shopping experience and provide a more accessible flow.
The general availability of Azure OpenAI Service
As Eric Boyd mentioned in his blog, we announced the general availability of Azure OpenAI Service as part of our ongoing partnership with OpenAI. Azure OpenAI Service provides a commercialization platform for businesses to leverage advanced AI models like GPT-3.5, Codex, and DALL*E to create innovative applications. Customers of all sizes across industries are using Azure OpenAI Service to do more with less, improve experiences for end users, and streamline operational efficiencies.
Microsoft Responsible AI Dashboard now available
The digitization of retail enables retailers to meet customer expectations with increasing precision, from providing personalized recommendations online to restocking inventory based on computer vision in physical stores. Shoppers expect the technology behind their retail experience to apply data and AI responsibly. In December, we began rolling out our new Responsible AI dashboard in Azure Machine Learning, which includes capabilities like fairness assessment, interpretability, error analysis, and causal inferencing. And today, we are excited to announce the general availability of the Microsoft Responsible AI dashboard. Retailers can leverage the Responsible AI dashboard to optimize the shopper’s experience as well as build trust and positive perception for their brands.
See how you can learn more about Responsible AI.
Full text search capabilities come to Azure Cosmos DB for Apache Cassandra
Azure Cosmos DB has seen tremendous momentum within the retail industry, given its ability to automatically and instantly scale when traffic is unpredictable without sacrificing performance or cost efficiency. Microsoft runs both the Windows store and Xbox Live on Azure Cosmos DB for this very purpose. This month, we’re announcing several performance enhancements to Azure Cosmos DB to make it easier and faster for developers to query data stores in Azure Cosmos DB. These include the ability to do full text searches in Azure Cosmos DB for Apache Cassandra through a native integration with Azure Cognitive Search, and support for GraphQL and REST through Data API builder.
For more technical detail about these and other updates to Azure Cosmos DB, visit our developer blog.
JSON support for Azure Cache for Redis Enterprise generally available
Support for JSON documents in Azure Cache for Redis Enterprise tiers, delivered via the RedisJSON module, has been made generally available as of November 2022. This turns Azure Cache for Redis Enterprise into a high-performance NoSQL document store and drives efficiency for developers to modernize their applications. The new RedisJSON module update is well suited to retail customers looking to store, search, and index product catalogs and shopper data via a single atomic operation.
To learn more on Azure Cache for Redis Enterprise and the RedisJSON module please check out the blog.
Microsoft named a Leader in the 2022 Gartner Magic Quadrant for Insight Engines
In December, Microsoft was named a leader in the 2022 Gartner Magic Quadrant for Insight Engines, which evaluates the capabilities of various vendors in the market for providing enterprise-scale search for app development. Organizations benefit, no matter the industry, but Cognitive Search is an exceptionally powerful tool for retailers, helping them to quickly find and analyze data related to customer behavior, sales, and inventory. It can also be used to personalize the shopping experience for individual customers based on their past interactions and preferences.
Learn more and download the report.
Customers innovating with Azure Data & AI
I’d like to close my inaugural “what’s new” blog post with my favorite way of making everything I’ve covered above actionable—by sharing examples of our customers succeeding. I share them as a way of helping spark the understanding—maybe even a little imagination—so that others can better envision how this amazing new Data & AI technology can be used in their own organizations.
I hope you enjoyed reading this month’s edition of what’s new in Azure Data & AI. We look forward to sharing more insights and inspiration in the months ahead. If you have a question or idea you’d like to hear perspective on, please share in the comments section.
Grupo Bimbo transforms the data analysis of commercial areas with Microsoft Solutions
Mexico—The global baking company carries out hundreds of thousands of transactions globally and needed a way for collaborators to quickly access and act on insights to improve sales. By adopting Power BI and Azure Synapse, Grupo Bimbo was able to unify internal and external data, increase business agility, and democratize insights for increased productivity across the organization. Read the full Case Study.
How online marketplace CDON used AI to become a market leader
Sweden—CDON began as a small online retailer of CDs, DVDs, and games and quickly became the Nordic’s largest online marketplace. When CDON realized legacy technologies were holding it back, the company looked to Azure to accelerate its innovation and scale. Now their developers use Azure Cognitive Search, Azure DevOps, and other services to better understand the user experience on their website and increase personalization for better performance. Read the full CDON Case Study.
How AEON Group increased profits and maximized inventory with data and AI
Japan—Since opening its first store in December 2005, Maibasuketto, a member of the AEON Group's supermarket business, has expanded its footprint at a pace of 100 stores every year. To ensure its growth was sustainable, the company embarked on an initiative to optimize store ordering and operations using Azure Cognitive Services, maximizing supply chain and inventory based on selling patterns throughout the day. Read the full AEON Group Case Study.
How Fashable reimagines fashion design with Azure Machine Learning and PyTorch
Portugal—Using Azure Machine Learning, Fashable created an AI application that generates original clothing designs to quickly get a pulse on consumer preferences. This helps fashion companies understand customer demand, get to market faster, and reduce clothing waste by only producing what they know will sell. Read the full Fashable Case Study.
WTW accelerates delivery time with Azure Migration and Analytics
United Kingdom—Operating in more than 140 countries with over 40,000 employees, Willis Towers Watson (WTW) has decades of experience working with the world’s largest loyalty programs. To efficiently govern the exponential growth in data for these programs and apply innovations in advanced analytics and AI, WTW moved its workload to Azure. Now their employees have more time to focus on uncovering insights for clients. Read the full WTW Case Study.
Quelle: Azure
Stellantis setzt auf erneuerbare Energien. Das Opel-Werk in Rüsselsheim soll sauberen Strom und saubere Wärme bekommen. (Geothermie, Elektroauto)
Quelle: Golem
Apple hat zwei neue Macbook Pro mit M2 Pro und M2 Max vorgestellt. Das Topmodell beinhaltet bis zu 96 GByte Speicher. (Macbook, Apple)
Quelle: Golem
