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  • AI as Competitor and Coworker – a thought starter for non-technical business leaders (Part 2)

    Part 2: Testing. Testing. 123 Testing

    Two key AI terms are training models and neural networks. A training model, well, you can train it and test the results, for example, with the OCR software we mentioned above. I have many invoices that I need to bring into my accounting software or ERP software. I can train AI to see what an invoice for my vendor looks like. Here’s where the account number is, and here’s the invoice date. Here’s the product I purchased, with the quantity and the amount.  If I train the AI model on ten or more examples of invoices, I receive somewhat similar invoices, and then the AI model learns the rules and can figure out what to do when it receives the next invoice. And, if the AI model puts a product number in an invoice number field, I know that the AI model made a mistake and that it is not meeting my expected results. I can then train the model more to handle that and similar scenarios.

    System testing is creating test criteria, testing against it, and comparing the results to what I expected.

    If you need to understand whether you have a positive or negative profit on a project, you could highlight your profit number with color to stand out. If the profit is positive, make the numbers green; if negative, red.

    If you copy the sample code below into a simple notepad text file and call it test.html, you can open that file with a browser and see a red negative five on the screen. These are specific instructions written in a language the software understands to tell the software to do something. The software follows the rules. You can open the file to run that software and confirm it functions as expected.

     

    <!DOCTYPE html>

    <html>

    <body>

    <h2>Number Color Indicator</h2>

    <p id=”demo” style=”color: green;”></p>

    <script>

    var num = -5; // Change this to any number you want

    document.getElementById(“demo”).style.color = num < 0 ? “red”: “green”;

    document.getElementById(“demo”).innerHTML = num;

    </script>

    </body>

    </html>

     

    You could change the number from -5 to 5 and see that the number is green when you open the newly saved version. The HTML language is comprised of standard commands. You tested the software to ensure it follows the rules. The more complex the rules and the bigger the program, the more difficult it is to test thoroughly.

    Many of us learned about testing hypotheses years ago in school. In the technology industry, system testing is testing the computer or software system. We define what we expect the results to be before running the test and then compare the system actions to see if they meet what we expect. Did the software put the invoice number in the invoice field and the product number in the product field? This testing process works when we have precise test data with clear conditions we are testing.

    With system testing, we trust the software we use daily and like to see our invoices correctly added to our accounting software. When something is wrong, we submit it to be “fixed.” We expect computer systems to provide repeatable, predictable, and accurate results.

    It’s not uncommon for people to say, “It seems like nobody tested this.” They think the software developers did not go through the process of defining test conditions, running the system to see if it produces those expected results, and comparing the actual to expected results to discern if the system is functioning as it needs to. If we ask software a question, we expect an accurate answer:  “The computer can’t be wrong.” “Numbers don’t lie.”

    If the computer system gives a different answer than we expected, the first thought could be, “What did I miss that my answer is different from the computer?” We may question our thinking before questioning the software. “System of Record” refers to the computer system that holds the “truth.”– accurate records you can trust. It is the place you can go and trust the data in that system to be accurate and true.

    Traditional software does what humans do but more accurately and faster, a paradigm left over from the industrial age, when automation increased output and machine labor replaced human labor.

    But AI is different.

    In the next blog post we will explore how.

    Don’t Go It Alone: Some ways TechHouse can help

    • Free Webinars to stay aware: Contact us for our upcoming webinars.
    • Check out Kathy’s AI panel on Bright Talk June 21st, 2024, at 1pm eastern.
    • Our CoPilot AwareTM Solution contains curated assessments, sample policies, communications, and guides for your AI Adoption journey.
    • Training and mentoring for you and your team: from Cybersecurity to Critical thinking workshops, our team is dedicated to transferring skills to help your team thrive in this new world.
    • Technical Preparedness and Tools: Engage us for an AI preparation, Data Governance, cybersecurity, or CoPilot/AI rollout in your organization.

  • AI as Competitor and Coworker – a thought starter for non-technical business leaders (Part 1)

    Imagine this: You’ve made it to the front of the line at the coffee shop, and with a laptop in one hand and drink in the other, you head over to the only seat available. As you settle your things and log in, you notice seated across the table is “Neurobot,” a sleek, silver-haired AI with an affinity for algorithms. It churns out content faster than a caffeinated squirrel on a keyboard. Your marketing agency, once the reigning content czar, now faces a formidable challenge.

    Neurobot’s digital ink flows effortlessly, creating blog posts, social media updates, and catchy slogans—all in the blink of a digital eye. Revenue slips away like sand through your fingers. How do you stand out when Neurobot’s content is as ubiquitous as cat memes? How do you differentiate yourself when your competitor’s AI whizzes through proposals faster than you can say “machine learning”?

    Several of my friends who run marketing agencies have a big challenge: their customers are using Artificial Intelligence (AI) to create content that they were creating less than a year ago. Revenue is lost. How do you differentiate yourself from AI as a competitor? How do you differentiate yourself if your albeit human competitor company uses AI to create what you’re creating far more efficiently – perhaps to build proposals or customer deliverables faster than you can without AI? What if you are a pathologist and AI starts reducing the time to evaluate images?

    Part 1: What is AI Software vs all the other software we have used in the past?

    AI mimics human intelligence’s ability to learn from experience and adjust to new inputs. How is AI software different from the accounting, spreadsheet, and graphic design software we have all used in the past? Well, those applications likely already use AI today, a type called classical machine learning. What is newer is Deep Learning AI. This is the AI that takes us from automation to decision-making, from tool to potential collaborator or competitor.

    But what is AI exactly? At TechHouse, we often say that you can’t use pronouns like “it” or “them” when trying to solve a problem because they can hide the problem you’re trying to solve. Similarly, we need to get good, clear terms if we’re going to be able to navigate a world where AI can both be a great partner and a significant competitor.

    AI is a broad term referring to many different specialized technologies. Like in medicine, the AI industry has different specializations to solve different problems. Although a podiatrist is a medical doctor, I would not ask them to cure my migraines. (Though, at this point, I may try that, too!) Similarly, only engaging the proper AI can help us reach our goals.

    Over the past 50-plus years, software has automated work. With a defined set of instructions, business software applications calculate numbers, generate reports, and store information for later access. We became accustomed to automated billing, payroll, and sales funnels. In the 1970s and 1980s, software programming combined with powerful machines to automate assembly lines and contributed to the rust belt. Job loss is a genuine concern, and creating new jobs is a real opportunity.

    Before AI existed, software did what humans told it to do and did it faster than a human. The instructions may have been complex, but they were instructions. We told the computer what to do, and it did it. We tested the software to ensure that the instructions provided were not flawed so that we could, in turn, ensure the behavior of the software. We could trust the software to perform functions, such as calculating math problems, determining a financial statement, and even in medical devices to monitor heartbeats, blood pressure, and more. Trust in these systems was due to rigorous testing of clearly defined instructions.

    AI software is different. AI does not carry out tasks with predefined rules. Instead, AI has a model, and humans train that model to make decisions. There are many different models, each suited for different types of decisions. The better suited the model is to the decision, and the more limited in scope and preciseness the testing, the more accurate and less probabilistic the answers may be.

    What is an example of a trained model? Optical Character Recognition (OCR) software. For example, OCR software for accountants can scan invoices, identify the invoice number, date, and amount, and then enter that information into the accounting software invoice record. How does it accomplish this? Someone has trained it on what those invoices look like and where to find the invoice number, date, and amount.

    Unlike traditional software, which works from a defined set of rules, AI software infers the answer from prior learning. It makes its best guess as to the right answer rather than following instructions to create an answer.

    To get the best guess, AI models data. AI models work from tremendously large datasets, such as the foundational models built on 40 years of digitized information from the Internet.

    In our next post, we will explore how this affects our understanding of AI and how we plan our business’s response.

    Don’t Go It Alone: Some ways TechHouse can help

    • Free Webinars to stay aware: Contact us for our upcoming webinars.
    • Check out Kathy’s AI panel on Bright Talk June 21st, 2024, at 1pm eastern.
    • Our CoPilot AwareTM Solution contains curated assessments, sample policies, communications, and guides for your AI Adoption journey.
    • Training and mentoring for you and your team: from Cybersecurity to Critical thinking workshops, our team is dedicated to transferring skills to help your team thrive in this new world.
    • Technical Preparedness and Tools: Engage us for an AI preparation, Data Governance, cybersecurity, or CoPilot/AI rollout in your organization.

  • Stop Artificial Intelligence (AI) from Increasing Unseen Bias in Our Organization

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    In a recent event, a business owner asked an important question – how can we stop AI from causing more significant problems in our organization? They gave an example of a team member often left out of emails due to personal conflicts or bias. How can we ensure their expertise is recognized when Microsoft 365’s CoPilot uses Microsoft Graph to analyze our emails for answers? Will it still see that person as an expert? If CoPilot uses Microsoft Graph to look at our emails and SharePoint files, is it just “crowd-sourcing” answers? If so, will it just highlight existing issues instead of unlocking our organization’s knowledge?

    We also need to consider different communication styles. For instance, how is their expertise captured and recognized if a team member is introverted or prefers face-to-face interactions over emails?

    Here are some strategies we came up with to tackle these challenges:

    Inclusive Communication Practices:

    Include all team members in relevant communications. Create email groups or Teams channels for every project. This way, everyone is informed and can share their ideas.

    AI Ethics Guidelines:

    Set rules for employees using AI tools like CoPilot and AI Builder. This way, AI won’t accidentally exclude certain team members. The aim is for AI tools to support human decision-making, not replace it.

    Regular Audits:

    Regularly check AI usage to find and fix any issues of bias or exclusion. Review the suggestions from CoPilot and the models built with AI Builder regularly to ensure they align with the organization’s values and ethics.

    Feedback Mechanisms:

    Set up ways for team members to report issues related to AI usage. This could be a dedicated Teams channel where employees can share their experiences and concerns.

    AI Training:

    Train employees on the ethical use of AI. Teach them how to use AI tools like CoPilot and AI Builder in a way that promotes inclusivity and fairness. This training could cover how to interpret and apply the suggestions from CoPilot and how to build and use models with AI Builder.

    By using these practices, businesses can promote a more inclusive and ethical use of AI.

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  • Microsoft’s CoPilot Products: What You Need to Know

    Microsoft’s CoPilot Products: What You Need to Know

    Since their launch in September 2023, less than a year ago, CoPilot products have undergone significant changes and improvements. This post will give you a brief overview of each of the CoPilot products, their main features, and how they can help you achieve your goals. You will also learn how CoPilot products are not just plug-and-play solutions but require careful planning, preparation, and customization to fit your specific needs and goals.

    How CoPilot Can Enhance Your Productivity

    Copilot’s AI-powered tools help you solve problems, create content, and research information. Depending on your subscription level, you can access different CoPilot products that suit your needs and preferences. Here are the main CoPilot products and what they can do for you:

    CoPilot in a Browser

    CoPilot in a browser is a free tool that lets you use AI to chat with Copilot, ask questions, generate content, and more. You can access it as CoPilot in Bing.

    CoPilot with Microsoft 365 Standard and Business Licenses

    If you have a Microsoft 365 Business Premium or Standard subscription, you can sign in to use CoPilot with an extra layer of security and protect your prompts from unintentional reuse. You can also use AI in productivity apps like Word, Excel, PowerPoint, and Outlook. To learn more, please read our “CoPilot Productivity Apps” article.

    CoPilot for Microsoft 365

    CoPilot for Microsoft 365 is a premium product that connects to your Microsoft 365 tenant. It can access exclusive information only known to your organization, such as:

    • Data stored in SharePoint and Email
    • Insights from Microsoft Graph

    Microsoft CoPilot for Sales and Service: Your AI-Boosted Helpers

    Microsoft CoPilot for Sales and CoPilot for Service are AI helpers that can enhance the productivity of your sales and service teams. Here are some of the things they can do for you:

    • Boost Your Productivity
      • CoPilot for Sales can help you save time, generate innovative ideas, build strong customer relationships, and close more deals.
      • CoPilot for Service can work with your contact center’s content sources to provide real-time responses, improving agent productivity and customer satisfaction.
    • Work Smoothly
      • CoPilot for Sales can provide real-time call insights, AI-generated meeting summaries, post-call analyses, and action items in Microsoft Teams. It can also craft contextual email responses, summarize lengthy threads, and create Teams Collaboration Spaces in Microsoft Outlook.
      • CoPilot for Service can integrate seamlessly with your existing workflow, whether that involves the agent console, Microsoft Teams, websites, or other apps.
    • Personalize with Data
      • CoPilot for Sales can use data from CRM platforms, large language models, Microsoft Graph, Microsoft 365 apps, and the Internet to personalize every customer interaction.
      • CoPilot for Service can connect directly to third-party knowledge bases like Salesforce, ServiceNow, and Zendesk. You can choose and manage the content sources that CoPilot for Service uses to provide real-time responses.
    • Streamline Your Workflow with AI
      • CoPilot for Sales can automatically connect external contacts to CRM contacts, streamlining your workflow and improving sales productivity.
      • CoPilot for Service can be extended with Microsoft CoPilot Studio. For example, you can boost your agent productivity even more with powerful CoPilot for Service actions. You can add more custom knowledge sources to the Copilot and set up authentication to ensure only authorized agents can use the Copilot to access information.

    Getting Started with CoPilot Products

    Like other business applications such as CRM or Accounting software, we recommend the following steps to get started with CoPilot products:

    • Select pilot and use scenarios for a staged rollout.
    • Prepare your tech platform, including data governance and cybersecurity.
    • Set up and configure your unique requirements.
    • Train team members and facilitate adoption.
    • Monitor and optimize performance.
    • Stay on top of the latest updates and changes in the CoPilot products.

    With the help of our friendly experts, you can make the most of your CoPilot product and enjoy the benefits of AI. We have extensive experience and knowledge in helping small businesses leverage CoPilot products for their specific needs and goals. We can also provide ongoing support and guidance to ensure your success with CoPilot products.

    Ready to Experience the Power of AI with CoPilot Products?

    Contact us to attend one of our planning workshops or schedule a one-on-one review. Don’t miss this opportunity to boost your productivity, creativity, and customer satisfaction with CoPilot products!

     

  • How Copilot Can Boost Your Productivity and Creativity with AI

    Jumpstart your AI Journey with Microsoft’s Copilot

    In the rapidly advancing world of technology, Artificial Intelligence (AI) has significantly impacted businesses, revolutionizing how we work and interact with data. One such AI innovation is Microsoft’s Copilot, a Generative AI chatbot.

    (Already a lot of new terms?  Check out our AI glossary blog. Wondering why everyone’s talking about AI? Check out our Why AI Matters blog.)

    What is Copilot?

    Copilot is an exceptionally capable AI that can converse with you.

    Before Generative AI Chatbots like Copilot, if we looked for answers to our questions via the internet, we would use a search engine like Google or Bing or go to a social forum like Reddit. All of these provide links to pre-created content that appears to be relevant and often does not have clear credentials. We then need to conduct further searches and validate those results to find an answer.

    Copilot does more than answer queries; it creates content – like emails, essays, blogs, and more.

    To map these ideas to AI concepts, Copilot is an exceptionally competent computer brain (artificial intelligence model) that can have a conversation (prompt and response) about what you ask in plain English (no coding required) and can create an answer unique to you and your question (generative AI).

    Want to learn more about AI in the Microsoft Cloud? Check outour blogs on AI.

    Why Should You Care about Copilot and AI?

    Microsoft found that most users save at least one hour a week using Copilot to augment their work, often many more. That’s more than 3X ROI in one month. However, your environment must be up to date with cybersecurity, data governance, and user training to fully leverage AI. If not, there is some technical debt you must pay down to move forward.

    Prepared organizations that take advantage of AI first will leapfrog those left behind. As shown in the chart at the top of this article, the change is faster and broader than the introduction of the Internet.

    Try it Out!

    If you want to try out Copilot, visit Microsoft Copilot at https://copilot.microsoft.com. If your questions involve sensitive data, like refining an annual plan or conducting product analysis, you may want to protect that data. If you have Microsoft Standard or Microsoft Business, you can log in with your account, and then your prompts are kept out of the AI’s model so others can’t see it when they converse with the model.

     

    Prompt Tips for Using Copilot

    Here are some tips for creating prompts or questions for Copilot:

    • Be Specific: Ask precise questions. For example, instead of “How to write an email?” ask, “How to write a professional email to a client regarding project updates?”.
    • Provide Context: Include relevant details. Share the formula and your goal if you need help with a spreadsheet formula.
    • Ask Directly: Don’t hesitate to ask Copilot directly. For instance, “What is the time difference between New York and London?” or “Help me draft a meeting agenda for a project kickoff.”
    • Use Proper Language: Ensure your prompts are grammatically correct and punctuated correctly for the best results.

     

    Prepare Your Environment

    • Microsoft Cloud Cybersecurity:To keep your data and devices safe, follow the best practices of cyber security, such as using multi-factor authentication (MFA) and applying security policies. Deploy AI solutions to detect and prevent breaches and attacks in the cloud and at the endpoint. Make sure your policies protect your data from unauthorized access or leakage.
    • Data Governance: SharePoint and OneDrive are great tools for storing and sharing files, but they can also become cluttered and chaotic if not managed properly. You may end up with multiple versions of the same document or files that are no longer relevant or accurate. If you use CoPilot to help you create content, you want it to use only the information that is appropriate and reliable for its decision-making. That’s why you must implement solid data governance policies defining how your files are organized, classified, and secured. You can’t rely on obscurity to protect your data. AI can find it if the user has the permission to see it.
    • Active Adoption and Change Management: Learning new technologies takes time and effort. In the past, many organizations relied on their team members to learn the technology independently, as they needed it. But with AI, CoPilot and other tools are evolving faster than ever. Waiting for your team to catch up will slow your adoption and productivity. You must engage your team early and often with training and updates on the new and deprecated features. This way, you can make the most of the AI tools and stay ahead of the curve.

    Want to learn more or move forward? Contact us.

  • Managing Azure AI Solutions

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    Managing Azure AI Solutions

    Welcome to our latest blog post in the Azure AI Services series. Today, we’re diving into the essential aspects of managing Azure AI solutions. From routine maintenance to disaster recovery, we’ll guide you through the key areas to focus on for optimal performance and compliance.

    Routine Maintenance Like any software, Azure AI solutions need regular upkeep for the best performance. This means monitoring system performance, updating software parts, and ensuring AI models are still accurate. For example, you may need to retrain your AI models with the latest data regularly. This could mean planning regular data updates and retraining sessions and setting up automatic pipelines to make these processes smoother.

    Handling Updates and Changes Azure often releases updates to its AI services. These can include new features, performance boosts, and security patches. It’s essential to keep up-to-date with these updates and implement them quickly. However, updates can sometimes bring changes that could affect your solution, so it’s vital to test your solution thoroughly after applying updates. This could mean setting up a test environment to check the updates before they’re rolled out to the live environment.

    Monitoring System Performance Keeping track of your Azure AI solution’s performance is key to making sure it continues to meet your business needs. Azure offers several tools for monitoring system performance, including Azure Monitor and Azure Application Insights. These tools can give you useful insights into your system’s performance and help you spot problems before they affect your business. For example, you might set up alerts in Azure Monitor to let you know when your solution’s performance falls below a certain level or use Azure Application Insights to track usage patterns and spot potential bottlenecks.

    Ensuring Continued Compliance Compliance is vital to managing Azure AI solutions, especially for businesses in regulated industries. Azure offers several tools to help make sure your solution stays compliant, including Azure Policy and Azure Blueprints. Regular audits can also help ensure your solution complies with all relevant laws and regulations. For example, you might set up Azure Policies to enforce specific compliance standards and use Azure Blueprints to set out and deploy a compliant architecture for your solution.

    Disaster Recovery and Backup A disaster recovery and backup strategy is vital for any business solution. Azure offers several backup and disaster recovery services, including Azure Backup and Azure Site Recovery. These services can help protect your solution from data loss and downtime, ensuring your business can keep running if a disaster happens. For example, you might set up Azure Backup to back up your data automatically at regular intervals and use Azure Site Recovery to copy your solution to a backup location for disaster recovery.

    That wraps up our discussion on managing Azure AI solutions. Remember, regular maintenance, staying updated with changes, monitoring system performance, ensuring compliance, and having a robust disaster recovery and backup strategy are all crucial for successfully managing your Azure AI solutions. Stay tuned to our blog for more posts in the Azure AI Services series, where we’ll continue to explore more topics to help you make the most of Azure’s powerful AI capabilities. Happy managing!

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  • Implementing Azure AI Services

    Implementing Azure AI Services

    Welcome back to our blog series on Azure AI Services. Our previous posts discussed choosing the right services for your business and provided examples of small business use cases. Today, we will provide an implementation guide and share the best practices.

    Implementing Azure AI Services

    Implementing Azure AI Services involves several steps, including setting up the service, integrating it with your application, and testing it. Here’s a general guide:

    1. Set Up the Service: After you’ve chosen the service you want to use, you’ll need to set it up in the Azure portal. This usually involves creating a new resource for the service and getting the API key. To do this, navigate to the Azure portal, select “Create a resource,” search for the service you want to use, and follow the prompts to create it. Once created, you can find the API key in the “Keys and Endpoint” section of the resource.
    2. Integrate with Your Application: You’ll need to integrate the service with your application. This usually involves adding the API key to your application and making API calls to the service. To integrate, you can use the Azure SDK for your programming language of choice. Import the relevant packages in your code, initialize the client for the service using the API key, and then make API calls as per the service’s documentation. You can find the API documentation for Azure AI Services on the Microsoft Learn Portal and the REST API reference.
    3. Test the Service: After integrating the service with your application, you should test it to be sure it works correctly. This might involve creating test cases and checking the results. For testing, consider using a testing framework suitable for your programming language. Create tests that cover various scenarios and edge cases. Compare the results from the service with expected outcomes to ensure accuracy.

    Best Practices to Follow

    When implementing Azure AI Services, there are several best practices you should follow:

    1. Secure Your API Keys: Your API keys are like passwords. It would be best if you kept them secure and never shared them.
    2. Handle Errors Gracefully: Your application should be able to handle errors from the service gracefully. This might involve retrying the request or showing an error message to the user.
    3. Monitor Your Usage: You should monitor your service usage to ensure you’re not exceeding your quota or spending more than you intended.

    In the next blog post, we’ll discuss tools to measure the impact of Azure AI Services on your business and how to interpret these metrics. Stay tuned!

    We hope this post has helped you understand how to implement Azure AI Services and the best practices to follow. As we progress through this series, we aim to provide you with an understanding of these services and how they can benefit your business. Remember, the future of your business could be powered by AI, and with Azure AI Services, that future is within your reach.

  • How to Measure the Impact of Azure AI Services on Your Business

    Measuring the Impact of Azure AI Services

    Welcome back to our blog series on Azure AI Services. Our earlier posts discussed implementing Azure AI Services and the best practices to follow. Today, we will discuss tools to measure the impact of Azure AI Services on your business and how to interpret these metrics.

    Tools to Measure the Impact

    Azure supplies several tools to help you measure the impact of AI Services on your business. These include:

    1. Azure Monitor: This service supplies full-stack monitoring, allowing you to collect, analyze, and act on telemetry data from your Azure and on-premises environments.
    2. Azure Application Insights: This is an extensible application performance management (APM) service that can help you understand the performance and usage of your live web applications.
    3. Azure Log Analytics: This service helps you collect and analyze data generated by resources in your cloud and on-premises environments.

    Interpreting the Metrics

    Interpreting the metrics involves understanding each metric and how it relates to the performance and impact of Azure AI Services on your business. Here are a few tips:

    1. Understand the Metrics: Each tool supplies different metrics. Understand what each metric stands for. For example, the number of API calls might stand for the usage of a service, while the response time might represent the performance of a service.
    2. Analyze Trends: Look for trends in the metrics. For example, increasing API calls might show increasing service usage.
    3. Correlate Metrics: Try to correlate different metrics. For example, if the response time increases as the number of API calls increases, it might show that the service is struggling to handle the load.

    Microsoft Learn has some good guidance for performance monitoring. For example, for Azure Search, see the article Analyze performance – Azure AI Search. Here are examples of performance metrics used to measure the success of various Azure AI Services:

    Azure OpenAI

    • HTTP Requests: The number of HTTP requests made to the service.
    • Tokens-Based Usage: The number of tokens used in the requests.
    • PTU Utilization: The PTU (Premium Turing Units) use for the service.
    • Fine-tuning Data: The data used for fine-tuning the models.

    Azure AI Search

    • Query Performance: Latency and throughput performance metrics.
    • Indexing Volume: The volume of data indexed by the service.

    Azure Vision

    • Precision: The percentage of identified classifications that were correct.
    • Recall: Actual classifications correctly identified (percentage).
    • Mean Average Precision (mAP): The average value of the average precision.

    Azure Speech

    • Latency: The time taken to transcribe speech to text.
    • Requests Per Second (RPS): The number of requests made to the service per second.

    Azure Language

    • Precision: Measures how precise/accurate your model is.
    • Recall: Measures the model’s ability to predict actual positive classes.
    • F1 score: The F1 score is a function of Precision and Recall.

    Azure Translator

    • Translation Accuracy: The accuracy of the translations provided by the service6.
    • Latency: The time taken to translate text.

    Azure Video Indexer

    • Transcription Accuracy: The accuracy of the transcriptions provided by the service8.
    • Speaker Recognition Accuracy: The accuracy of the speaker recognition feature8.

    Azure Immersive Reader

    • User Engagement: The number of users engaging with the service and the duration of their engagement.
    • Reading Comprehension Improvement: The improvement in reading comprehension for service users.

    Azure Content Safety

    • Severity Indication: A unique ‘Severity’ metric that shows the severity of specific content on a scale ranging from 0 to 7.
    • Technical metrics (latency, accuracy, recall) and business metrics (block rate, block volume, category proportions, language proportions, and more).

    We hope this post has helped you understand how to measure the impact of Azure AI Services on your business and the metrics to consider for monitoring these services. As we progress through this series, we aim to provide you with a comprehensive understanding of these services and how they can help your business. Remember, the future of your business could be powered by AI, and with Azure AI Services, that future is within your reach.

     

  • How to Get Started with Azure AI Services in 2024 | Azure Blog

    Create your first Azure AI Services

    Welcome back to our blog series on Azure AI Services. In our previous posts, we introduced you to Azure AI Services and discussed its various services. Today, we will guide you on how to get started with Azure AI Services and understand its pricing model.

    Setting Up Azure AI Services

    You must have an Azure account before you can use Azure AI Services. If you don’t have one, you can create a free account. Once you have an Azure account, you can create an AI Services resource in the Azure portal. Here are the steps:

    1. Sign into the Azure portal.
    2. In the left-hand menu, click on “Create a resource.”
    3. In the “New” window, search for “AI Services.”
    4. In the search results, select “AI Services” and then click “Create”.
    5. Fill in the required fields such as “Name,” “Subscription,” “Resource Group,” “Location,” and “Pricing Tier.”
    6. Click “Review + Create” and “Create” to create your AI Services resource.

    Once the Azure AI Service resource is created, you can explore the various Azure AI Services. For example, to explore Azure  AI Vision, go to Vision Studio here: Vision Studio (azure.com).

    Understanding the Pricing Model

    Azure AI Services uses a consumption-based pricing model. This means you pay for the transactions you make. The cost depends on the type of service and the tier you choose. Some services offer a free tier with a limited number of monthly transactions.

    You can monitor your usage and manage your costs in the Azure portal. Understanding the pricing model is essential to ensure that you choose the right services for your business and stay within your budget.

    We hope this post has helped you explore Azure AI Services and its pricing model. As we progress through this series, we aim to provide you with a comprehensive understanding of these services and how they can benefit your business. Remember, the future of your business could be powered by AI, and with Azure AI Services, that future is within your reach.

  • Understanding Azure AI Services

    Understanding Azure AI Services

    Welcome back to our blog series on Azure AI Services. Our previous post gave you an overview of Azure AI and its benefits. Today, we’ll go deeper into each Azure AI Service, explain what they do, and show how they can help your business.

    Azure OpenAI

    Azure OpenAI is a service that lets you do many things with words. You can use it to:

    • Write new text
    • Translate text to different languages
    • Summarize long text
    • And more

    For example, a law firm can use Azure OpenAI to review legal documents faster. The service can learn legal words and find important information, saving lawyers time and money.

    Azure AI Search

    Azure AI Search is a service that helps you find what you need on the web. You can use it to:

    • Improve your customer experience by showing relevant search results.
    • Search different types of web content, such as web pages, mobile apps, and enterprise websites.
    • Personalize search results based on the customer’s preferences and behavior.

    For example, an online clothing store can use Azure AI Search to improve its website’s search function. The service can show search results that match the customer’s likes and past purchases.

    Azure Vision

    Azure Vision is a service that helps you understand images and videos. You can use it to:

    • Classify images into categories.
    • Detect objects in images and videos.
    • Recognize text in images and videos.

    For example, a distribution company can use Azure Vision to manage its inventory. The service can scan images from the warehouse and identify products, check their quality, and track their location.

    Azure Speech

    Azure Speech is a service that helps you convert speech to text and text to speech. You can use it to:

    • Transcribe speech in real-time.
    • Use voice commands to control your applications.
    • Make text sound like speech.

    For example, a healthcare provider can use Azure Speech to record patient interactions. The service can help them keep accurate records and free up time for patient care.

    Azure Language

    Azure Language is a service that helps you build applications with natural language understanding. You can use it to:

    • Create chatbots that can talk to your customers.
    • Automate customer service tasks.
    • Analyze customer feedback and sentiment.

    For example, a retail company can use Azure Language to provide 24/7 customer support. The service can create an AI-powered bot that can answer common questions, process returns, and recommend products.

    Azure Translator

    Azure Translator is a service that helps you translate text in real-time across multiple languages. You can use it to:

    • Build applications and websites that work in different languages.
    • Translate emails, documents, and live conversations.
    • Support multilingual communication and collaboration.

    For example, a global consulting firm can use Azure Translator to communicate with clients worldwide. The service can translate emails, documents, and live conversations in real-time.

    Azure Document Intelligence

    Azure Document Intelligence is a service that helps you get information and insights from your documents. You can use it to:

    • Automate data entry tasks.
    • Extract insights from unstructured data.
    • And more.

    For example, a hospital can use Azure Document Intelligence to digitize its patient records. The service can get key information from scanned documents, making it easier to search and analyze patient data.

    Azure Video Indexer

    Azure Video Indexer is a service that helps you get insights from your videos. You can use it to:

    • Transcribe speech in videos.
    • Recognize speakers in videos.
    • Summarize videos.

    For example, a non-profit organization can use Azure Video Indexer to analyze event footage. The service can find important moments, transcribe speeches, and recognize attendees.

    Azure Immersive Reader

    Azure Immersive Reader is a service that helps you make your text content more accessible and engaging. You can use it to:

    • Improve reading comprehension, pronunciation, and fluency.
    • Read text aloud, highlight words, and adjust font size and color.
    • And more.

    For example, a school can use Azure Immersive Reader to help students with learning difficulties. The service can read text aloud, highlight words, and adjust font size and color.

    Azure Content Safety

    Azure Content Safety is a service that helps you detect unwanted content. You can use it to:

    • Monitor and moderate content.
    • Ensure a safe and positive user experience.
    • And more.

    For example, a digital marketing agency can use Azure Content Safety to moderate user-generated content on their clients’ websites. The service can detect inappropriate or harmful content, ensuring a safe and positive user experience.

    In the next blog post, we’ll discuss best practices for planning to use these Azure AI Services. We’ll cover factors to consider, cost analysis, and how to identify the exemplary service for your business needs. Stay tuned!

    This blog post is part of a series on Azure AI Services. Follow our blog to stay updated with new posts.