Monat: September 2023

How we interact with information: The new era of search

In today’s rapidly evolving technological landscape, generative AI, and especially Large Language Models (LLMs), are ushering in a significant inflection point. These models stand at the forefront of change, reshaping how we interact with information.

The utilization of LLMs for content consumption and generation holds immense promises for businesses. They have the potential to automate content creation, enhance content quality, diversify content offerings, and even personalize content. This is an inflection point and great opportunity to discover innovative ways to accelerate your business’s potential; explore the transformative impact and shape your business strategy today.

LLMs are finding practical applications in various domains. Take, for example, Microsoft 365 Copilot—a recent innovation aiming to reinvent productivity for businesses by simplifying interactions with data. It makes data more accessible and comprehensible by summarizing email threads in Microsoft Outlook, highlighting key discussion points, suggesting action items in MicrosoftTeams, and enabling users to automate tasks and create chatbots in Microsoft Power Platform.

Data from GitHub demonstrates the tangible benefits of Github Copilot, with 88 percent of developers reporting increased productivity and 73 percent reporting less time spent searching for information or examples.

Transforming how we search

Remember the days when we typed keywords into search bars and had to click on several links to get the information we needed?

Today, search engines like Bing are changing the game. Instead of providing a lengthy list of links, they intelligently interpret your question and source from various corners of the internet. What’s more, they present the information in a clear and concise manner, complete with sources.

The shift in online search is making the process more user-friendly and helpful. We are moving from endless lists of links towards direct, easy-to-understand answers. The way we search online has undergone a true evolution.

Now, imagine the transformative impact if businesses could search, navigate, and analyze their internal data with a similar level of ease and efficiency. This new paradigm would enable employees to swiftly access corporate knowledge and harness the power of enterprise data. This architectural pattern is known as Retrieval Augmented Generation (RAG), a fusion of Azure Cognitive Search and Azure OpenAI Service—making this streamlined experience possible.

The rise of LLMs and RAG: Bridging the gap in information access

RAG is a natural language processing technique that combines the capabilities of large pre-trained language models with external retrieval or search mechanisms. It introduces external knowledge into the generation process, allowing models to pull in information beyond their initial training.

Here’s a detailed breakdown of how RAG works:

Input: The system receives an input sequence, such as a question that needs an answer.

Retrieval: Prior to generating a response, the RAG system searches for (or “retrieves”) relevant documents or passages from a predefined corpus. This corpus could encompass any collection of texts containing pertinent information related to the input.

Augmentation and generation: The retrieved documents merge with the original input to provide context. This combined data is fed into the language model, which generates a response or output.

RAG can tap into dynamic, up-to-date internal and external data sources, and can access and utilize newer information without requiring extensive training. The ability to incorporate the latest knowledge leads to better precise, informed, and contextually relevant responses that brings a key advantage.

RAG in action: A new era of business productivity

Here are some scenarios where RAG approach can enhance employee productivity:

Summarization and Q&A: Summarize massive quantitates of information for easier consumption and communication.

Data-driven decisioning: Analyze and interpret data to uncover patterns, and identify trends to gain valuable insights.

Personalization: Tailor interactions with individualized information to result in personalized recommendations.

Automation: Automate repetitive tasks to streamline and be more productive.

As AI continues to evolve, its applications across various fields are becoming increasingly pronounced.

The RAG approach for financial analysis

Consider the world of financial data analysis for a major corporation—an arena where accuracy, timely insights, and strategic decision-making are paramount. Let’s explore how RAG use cases can enhance financial analysis with a fictitious company called Contoso.

1. Summarization and Q&A

Scenario: ‘Contoso’ has just concluded its fiscal year, generating a detailed financial report that spans hundreds of pages. The board members want a summarized version of this report, highlighting key performance indicators.

Sample prompt: “Summarize the main financial outcomes, revenue streams, and significant expenses from ‘Contoso’s’ annual financial report.”

Result: The model provides a concise summary detailing ‘Contoso’s total revenue, major revenue streams, significant costs, profit margins, and other key financial metrics for the year.

2. Data-driven decisioning

Scenario: With the new fiscal year underway, ‘Contoso’ wants to analyze its revenue sources and compare them to its main competitors to better strategize for market dominance.

Sample prompt: “Analyze ‘Contoso’s revenue breakdown from the past year and compare it to its three main competitors’ revenue structures to identify any market gaps or opportunities.”

Result: The model presents a comparative analysis, revealing that while ‘Contoso’ dominates in service revenue, it lags in software licensing, an area where competitors have seen growth.

3. Personalization

Scenario: ‘Contoso’ plans to engage its investors with a personalized report, showcasing how the company’s performance directly impacts their investments.

Sample prompt: “Given the annual financial data, generate a personalized financial impact report for each investor, detailing how ‘Contoso’s’ performance has affected their investment value.”

Result: The model offers tailored reports for each investor. For instance, an investor with a significant stake in service revenue streams would see how the company’s dominance in that sector has positively impacted their returns.

4. Automation

Scenario: Every quarter, ‘Contoso’ receives multiple financial statements and reports from its various departments. Manually consolidating these for a company-wide view would be immensely time-consuming.

Sample prompt: “Automatically collate and categorize the financial data from all departmental reports of ‘Contoso’ for Q1 into overarching themes like ‘Revenue’, ‘Operational Costs’, ‘Marketing Expenses’, and ‘R&D Investments’.”

Result: The model efficiently combines the data, providing ‘Contoso’ with a consolidated view of its financial health for the quarter, highlighting strengths and areas needing attention.

LLMs: Transforming content generation for businesses

Leveraging RAG based solutions, businesses can boost employee productivity, streamline processes and make data-driven decisions. As we continue to embrace and refine these technologies, the possibilities for their application can be virtually limitless.

Where to start?

Microsoft provides a series of tools to suit your needs and use cases.

Learn more about using your data with Azure OpenAI Service.

What is Azure Machine Learning prompt flow?

Orchestrate your AI with Semantic Kernel.

Discover a sample app for the RAG pattern using Azure Cognitive Search and Azure OpenAI.

Learn more

Check out below partner solutions for a jumpstart.

Learn about generative AI with Avanade.

Discover generative AI technology services through Accenture.

Explore EY’s AI consulting Services.

PwC provides AI everywhere.

KPMG presents speed to modern data, analytics, and AI.

Integration of RAG into business operations is not just a trend, but a necessity in today’s data-driven world. By understanding and leveraging these solutions, businesses can unlock new avenues for growth and productivity.

The post How we interact with information: The new era of search appeared first on Azure Blog.
Quelle: Azure

Microsoft and Adobe partner to deliver cost savings and business benefits

Delivering quality end-to-end digital experiences can be challenging for multiple reasons including lack of resources, legacy technologies, and disorganized customer journeys. Microsoft and Adobe have purpose-built integrations to overcome these challenges with a result of simplifying deployment and reducing overall cost.

Grounded in open software standards and a scalable, secure cloud, Microsoft and Adobe deliver end-to-end technology ecosystems for a modern, secure, and connected enterprise. The integration between our applications transforms data into insights that enable intelligent, targeted, and customized marketing campaigns. With Microsoft setting the data foundation and Adobe providing a comprehensive marketing activation layer, we can take organizations from traditional batch marketing to real-time, precise, and timely event-based marketing.

Marketing needs to be tailored specifically to how, when, and where customers want to shop. To achieve this, Microsoft and Adobe are providing tools to make collaboration between employees easier and smarter, such as digital signatures and document sharing. Meanwhile, automation tools enable organizations to source, adapt, and deliver assets for more personalized customer experiences. These sophisticated integrations between Microsoft and Adobe are enabling businesses to do more with less.

Microsoft and Adobe partner to create a connected enterprise

A 2023 commissioned study conducted by Forrester Consulting—The Total Economic Impact™ Of Adobe SaaS Solutions with Microsoft Cloud—uncovered how our partnership and technology collaboration with Adobe lowers time and resource costs, improves employee productivity and customer engagement, as well as the return on investment by deploying Adobe applications for the enterprise on the Microsoft Cloud. The study specifically focuses on Adobe Experience Cloud and Adobe Document Cloud running on Microsoft Azure, Microsoft 365, and Microsoft Dynamics 365.

The study explores the challenges organizations hope to address by implementing the integrated Microsoft and Adobe solutions (see image below).

The resounding solutions to these challenges show the strength of the native integration between Microsoft and Adobe SaaS solutions, which enabled organizations to:

Enhance customer experiences by leveraging consolidated customer data, and with cloud support across solutions, gain real-time insights and analysis about these customers and marketing efforts.

Strengthen security and protection of data files across the enterprise with tightly integrated tools. With consolidated tools under an all-in-one vendor, tool deployment and IT team management is simplified.

Streamline data access and management across the organization. With tight integrations, organizations can improve collaboration, decision-making, and performance among their teams.

Integrated data means better customer journeys

As customers move between digital channels—mobile apps, social media, online chat, and so on—they generate digital records. Capturing insights from siloed data streams can be a challenge, impacting the ability to create personalized experiences in a timely manner for customers. By transitioning companies from legacy and siloed technologies to a connected, cloud-enabled tech stack, Microsoft and Adobe address the data issues that improve the quality of connections companies have to their customers.

According to the Forrester study, those implementing Adobe SaaS solutions on Microsoft Cloud say that managing customer experiences is the top goal of their organization (71 percent). This is also where organizations found key benefits of implementing integrated solutions.

Some survey participants saw a 45 percent gain in customer loyalty that they attributed to integrating Microsoft Cloud with Adobe Experience Cloud.

Data analysis was 15 percent faster, which improved the overall speed of work for marketers by 10 percent.

Customer satisfaction, the number of transactions, and customer retention rates all increased due to the data-driven capabilities the integration presents.

AI and Machine Learning are pivotal in sifting through large volumes of data, pinpointing areas of importance or anomalies for marketers. The wide range of triggering events, including email, calendar invites, webinars, advertising, data changes, and ERP events offer a holistic view of the customer. A focus on a self-service model delivers speed and ease of use. In combination, organizations gain operational efficiencies, as well as fast, effective marketing from Microsoft and Adobe integrations.

Increase data security across the enterprise

Keeping the organizations’ data and files secure is a leading concern of enterprise leaders—especially with data spread between on-prem servers and the cloud and hybrid work arrangements. The fear is that, without tighter integration with cloud security controls, their data centers are vulnerable to attacks.

Security is inherent with the cloud. Additionally, data stored in Microsoft Cloud and imported directly into natively connected Adobe solutions with native connectors reduces data vulnerabilities. Interviewees of the Forrester study reported that data and files specific to Adobe had not experienced any compromises.

Reduce time and resource demands to streamline deployment

Without the tight integration between Microsoft and Adobe, IT teams—or consultants and contractors—need to code or build custom API connectors to implement solutions. Microsoft and Adobe have developed over 60 specific integrations that are purpose-built for common use cases, reducing the workload for IT teams in customer organizations. The Forrester study found that for the composite organization the integration of Adobe tools went from a multi-month endeavor down to one month and required far fewer resources. This led to overall IT and security team productivity going up by 20 percent. One interviewed company reallocated 15 members of their 40-person IT team to other organizational projects. The Forrester study found that a composite organization based on interviewed customers saw the following financial benefits over three years:

251 percent ROI

1.3M USD Benefits PV

925 Thousand USD Net Present Value

Because Microsoft and Adobe support global content distribution, with Microsoft having cloud storage services worldwide, organizations can increase efficiencies in creating and distributing the millions of content assets needed to create personalized customer journeys.

Read the study to learn more

The Total Economic Impact™ Of Adobe SaaS Solutions with Microsoft Cloud report provides a deep analysis of the key challenges organizations look to address when deploying Adobe SaaS solutions on Microsoft Cloud. Read the full study to understand why.

As global leaders in business solutions, Microsoft and Adobe combine the power of data and expertise in marketing to deliver innovations that are reliable, secure, and optimized to meet consumers wherever they are in the customer journey.
The post Microsoft and Adobe partner to deliver cost savings and business benefits appeared first on Azure Blog.
Quelle: Azure

Manage your big data needs with HDInsight on AKS

As companies today look to do more with data, take full advantage of the cloud, and vault into the age of AI, they’re looking for services that process data at scale, reliably, and efficiently. Today, we’re excited to announce the upcoming public preview of HDInsight on Azure Kubernetes Service (AKS), our cloud-native, open-source big data service, completely rearchitected on Azure Kubernetes Service infrastructure with two new workloads and numerous improvements across the stack. The public preview will be available for use on 10/10.

HDInsight on AKS amplifying performance

HDInsight on AKS includes Apache Spark, Apache Flink, and Trino workloads on an Azure Kubernetes Service infrastructure, and features deep integration with popular Azure analytics services like Power BI, Azure Data Factory, and Azure Monitor, while leveraging Azure managed services for Prometheus and Grafana for monitoring. HDInsight on AKS is an end-to-end, open-source analytics solution that is easy to deploy and cost-effective to operate. 

HDInsight on AKS helps customers leverage open-source software for their analytics needs by: 

Providing a curated set of open-source analytics workloads like Apache Spark, Apache Flink, and Trino. These workloads are the best-in-class open-source software for data engineering, machine learning, streaming, and querying.

Delivering managed infrastructure, security, and monitoring so that teams can spend their time building innovative applications without needing to worry about the other components of their stack. Teams can be confident that HDInsight helps keep their data safe. 

Offering flexibility that teams need to extend capabilities by tapping into today’s rich, open-source ecosystem for reusable libraries, and customizing applications through script actions.

Customers who are deeply invested in open-source analytics can use HDInsight on AKS to reduce costs by setting up fully functional, end-to-end analytics systems in minutes, leveraging ready-made integrations, built-in security, and reliable infrastructure. Our investments in performance improvements and features like autoscale enable customers to run their analytics workloads at optimal cost. HDInsight on AKS comes with a very simple and consistent pricing structure per vcore per hour regardless of the size of the resource or the region, plus the cost of resources provisioned.

Developers love HDInsight for the flexibility it offers to extend the base capabilities of open-source workloads through script actions and library management. HDInsight on AKS has an intuitive portal experience for managing libraries and monitoring resources. Developers have the flexibility to use a Software Development Kit(SDK), Azure Resource Manager (ARM) templates, or the portal experience based on their preference.

Join us for a deep dive into this launch in our upcoming free webinar. 

Open, managed, and flexible

HDInsight on AKS covers the full gamut of enterprise analytics needs spanning streaming, query processing, batch, and machine learning jobs with unified visualization. 

Curated open-source workloads

HDInsight on AKS includes workloads chosen based on their usage in typical analytics scenarios, community adoption, stability, security, and ecosystem support. This ensures that customers don’t need to grapple with the complexity of choice on account of myriad offerings with overlapping capabilities and inconsistent interoperability.  

Each of the workloads on HDInsight on AKS is the best-in-class for the analytics scenarios it supports: 

Apache Flink is the open-source distributed stream processing framework that powers stateful stream processing and enables real-time analytics scenarios. 

Trino is the federated query engine that is highly performant and scalable, addressing ad-hoc querying across a variety of data sources, both structured and unstructured.  

Apache Spark is the trusted choice of millions of developers for their data engineering and machine learning needs. 

HDInsight on AKS offers these popular workloads with a common authentication model, shared meta store support, and prebuilt integrations which make it easy to deploy analytics applications.

Managed service reduces complexity

HDInsight on AKS is a managed service in the Azure Kubernetes Service infrastructure. With a managed service, customers aren’t burdened with the management of infrastructure and other software components, including operating systems, AKS infrastructure, and open-source software. This ensures that enterprises can benefit from ongoing security and functional and performance enhancements without investing precious development hours.  

Containerization enables seamless deployment, scaling, and management of key architectural components. The inherent resiliency of AKS allows pods to be automatically rescheduled on newly commissioned nodes in case of failures. This means jobs can run with minimal disruptions to Service Level Agreements (SLAs). 

Customers combining multiple workloads in their data lakehouse need to deal with a variety of user experiences, resulting in a steep learning curve. HDInsight on AKS provides a unified experience for managing their lakehouse. Provisioning, managing, and monitoring all workloads can be done in a single pane of glass. Additionally, with managed services for Prometheus and Grafana, administrators can monitor cluster health, resource utilization, and performance metrics.  

Through the autoscale capabilities included in HDInsight on AKS, resources—and thereby cost—can be optimized based on usage needs. For jobs with predictable load patterns, teams can schedule the autoscaling of resources based on a predefined timetable. Graceful decommission enables the definition of wait periods for jobs to be completed before ramping down resources, elegantly balancing costs with experience. Load-based autoscaling can ramp resources up and down based on usage patterns measured by compute and memory usage. 

HDInsight on AKS marks a shift away from traditional security mechanisms like Kerberos. It embraces OAuth 2.0 as the security framework, providing a modern and robust approach to safeguarding data and resources. In HDInsight on AKS authorization, access controls are based on managed identities. Customers can also bring their own virtual networks and associate them during cluster setup, increasing security and enabling compliance with their enterprise policies. The clusters are isolated with namespaces to protect data and resources within the tenant. HDInsight on AKS also allows management of cluster access using Azure Resource Manager (ARM) roles. 

Customers who’ve participated in the private preview love HDInsight on AKS. 

Here’s what one user had to say about his experience. 

“With HDInsight on AKS, we’ve seamlessly transitioned from the constraints of our in-house solution to a robust managed platform. This pivotal shift means our engineers are now free to channel their expertise towards core business innovation, rather than being entangled in platform management. The harmonious integration of HDInsight with other Azure products has elevated our efficiency. Enhanced security bolsters our data’s integrity and trustworthiness, while scalability ensures we can grow without hitches. In essence, HDInsight on AKS fortifies our data strategy, enabling more streamlined and effective business operations.” 
Matheus Antunes, Data Architect, XP Inc

Azure HDInsight on AKS resources

Learn more about Azure Kubernetes Service (AKS) 

The post Manage your big data needs with HDInsight on AKS appeared first on Azure Blog.
Quelle: Azure