Author: Naveen Raj

Azure Virtual Machines: Updates for August 2023

Azure Virtual Machines: Updates for August 2023

Azure VM Updates in August 2023

Azure Virtual Machines (VMs) are undeniably one of the cornerstones of Azure, offering flexible and scalable compute resources for a multitude of workloads. In this blog post, we’ll delve into the latest updates and features for Azure VMs that were unveiled in August 2023.

Azure Advisor’s Availability Zone Recommendation

Azure Advisor now offers an Availability Zone recommendation feature to enhance VM resiliency. This capability assists you in migrating VMs to availability zones within an Azure region, ensuring high availability and fault tolerance for your applications. Azure Advisor takes a deep dive into your VMs, considering performance, cost, and compliance factors. Once the analysis is complete, you can effortlessly implement the recommendation with just a few clicks. Alternatively, Azure Resource Manager templates or PowerShell scripts can automate the process. The good news is that this feature is now generally available.

Cross Subscription Restore for Azure Virtual Machines

Another exciting feature that has become generally available is Cross Subscription Restore for Azure VMs. This capability lets you restore your VMs from a backup to a different subscription within the same Azure Active Directory tenant. It’s an incredibly useful tool, especially in disaster recovery, testing, or migration scenarios. Whether you prefer the Azure portal, Azure CLI, or PowerShell, you have the flexibility to perform cross-subscription restore operations.

Retirement of ND-series and NC-series VMs

In a significant move, Azure will be retiring ND-series and NC-series VMs, powered by NVIDIA Tesla P40 and K80 GPUs, on August 31, 2023. These VMs are making way for newer GPU VMs that offer higher performance and support newer CUDA compute capability levels. If your operations rely on ND-series or NC-series VMs, planning your migration to these newer GPU VMs before the retirement date arrives is essential. To assist you in this transition, you can utilize Azure Migrate or other similar tools to assess your readiness for migration and facilitate the process.

Azure NetApp Files Cloud Backup for Virtual Machines

Introducing Azure NetApp Files Cloud Backup for Virtual Machines is a noteworthy development. This feature empowers you to back up and restore your Azure VMs leveraging Azure NetApp Files as their storage solution. Azure NetApp Files is a fully managed file storage service known for its high performance, scalability, and robust security for file-based workloads. Cloud Backup protects your data from accidental deletion, corruption, or even ransomware attacks. You can configure backup policies, set schedules, and define retention periods per your business needs. Moreover, you can restore your data to any point within the retention period. As of now, this feature is in the public preview stage.

In conclusion, these are just some of the highlights of the Azure VM updates that unfolded in August 2023. For a comprehensive look at these updates and more, please explore the Azure updates page or check out the Azure blog.

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Exploring the Exciting Azure SQL Updates Unveiled in August 2023

Azure SQL Updates for August 2023

Azure SQL Updates

Azure SQL offers secure SQL database engines for applications. This post highlights August 2023 updates.

External REST Endpoint Invocation

Now, let’s explore External REST Endpoint Invocation. Microsoft Build 2023 introduced it—a way to integrate Azure Services into Azure SQL Database effortlessly with just one line of code. This feature enriches data, handles complex calculations, and invokes machine learning models. For instance, you can analyze customer reviews by calling an Azure Function via an external REST endpoint.

XML Compression for Azure SQL

Next, focus on the second feature, available since August 2023. XML compression reduces storage needs for Azure SQL Database and Azure SQL Managed Instance by compressing off-row XML data. Using ALTER INDEX, apply XML compression to existing XML indexes to reduce storage space by up to 75%.

Always Encrypted with Intel SGX Enclaves

Always Encrypted secures sensitive data within client applications. Extend it with secure enclaves, enabling computations on encrypted data within the database server’s protected memory region. You can perform operations like pattern matching and sorting on encrypted data without decryption.

In August 2023, Always Encrypted with Intel SGX enclaves entered public preview for Azure SQL Database on DC-series hardware with up to 40 vCores, offering better performance.

New JSON Type and JSON Aggregates

Without delay, explore Azure SQL’s latest JSON features. Since 2016, Azure SQL has supported JSON as a native data type. It lets you store and query JSON documents using standard SQL operators and functions. August 2023 introduced two new features: a lightweight JSON type and JSON aggregates.

Currently in public preview, the new JSON type outperforms NVARCHAR(MAX) by offering faster parsing, better storage efficiency, JSON Schema support, partial updates using JSON Patch, and change tracking with JSON Diff.

The JSON aggregates are a set of new aggregate functions that allow you to perform calculations on JSON values and return JSON results. These functions include:

  • JSON_ARRAYAGG: This returns a JSON array containing the aggregated values.
  • JSON_OBJECTAGG: It Returns a JSON object containing the aggregated key-value pairs.
  • JSON_QUOTE: Function returns a JSON string containing the quoted value.
  • JSON_MERGE: Returns a JSON value that results from merging two or more JSON values.

Conclusion

In summary, these updates and enhancements from August 2023 strengthen Azure SQL, helping you build more powerful, secure applications.

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Azure PostgreSQL Updates for August 2023

Azure PostgreSQL Updates for August 2023

Azure PostgreSQL Updates: Overview

If you are using Azure PostgreSQL, you might be interested in the latest updates and enhancements released in August 2023. It is a fully managed service that offers high availability, scalability, security, and performance for PostgreSQL applications.

Minor Version Updates

Azure PostgreSQL supports multiple minor versions of PostgreSQL, from 11 to 15. In August 2023, PostgreSQL released the latest minor version updates for all supported major versions. These updates include bug fixes, security patches, and performance improvements. You can find the detailed release notes for each minor version on the official PostgreSQL website.

The latest minor versions are:

  • 11.21
  • 12.16
  • 13.12
  • 14.9
  • 15.4

You can upgrade your PostgreSQL server to the latest minor version using the Azure portal, Azure CLI, or PowerShell. Upgrading to the latest minor version is recommended to ensure optimal performance and security for your PostgreSQL server.

Azure PostgreSQL: PgBouncer Support

PgBouncer is a lightweight connection pooler for PostgreSQL that reduces the overhead of opening and closing connections. It can improve the scalability and throughput of your PostgreSQL applications by reusing existing connections and balancing the load among them.

PostgreSQL now supports PgBouncer version 1.20 for all PostgreSQL versions in all supported regions. You can enable PgBouncer for your Azure PostgreSQL server using the Azure portal, Azure CLI, or PowerShell. Also, configure various PgBouncer settings, such as pool mode, max connections, idle timeout, and more.

PgBouncer provides metrics that you can monitor using Azure Monitor or other tools. You can view metrics such as active connections, waiting requests, server latency, etc. These metrics can help you troubleshoot issues and optimize your PgBouncer performance.

Conclusion

The Azure PostgreSQL is constantly adding new features and updates to provide the best experience for PostgreSQL users. In August 2023, it released minor version updates and PgBouncer support for all PostgreSQL versions. These features can help you enhance your PostgreSQL applications with high availability, scalability, security, and performance.

To learn more, visit the official documentation or sign up for a free trial today.

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Amazon Translate: Real-Time Language Mastery

Amazon Translate: Real-Time Language Mastery

Overview

Amazon Translate is a fully managed service that enables you to translate text and speech in real time. It can automatically translate text between multiple languages, making it useful for tasks like localizing content, providing multi-language support in applications, and improving global communication. You can use it to communicate with customers, partners, and employees across the globe.

Features and Benefits

Here are some of the features and benefits of Amazon Translate:

  • Supports over 70 languages and variants, including Arabic, Chinese, English, French, German, Hindi, Japanese, Portuguese, Russian, and Spanish.
  • Uses deep learning models trained on a large and diverse text and speech data corpus. This ensures high-quality and natural-sounding translations.
  • Offers neural machine translation (NMT) and automatic speech recognition (ASR) capabilities. You can use NMT to translate text from one language to another and ASR to convert speech to text in the same or a different language.
  • Integrates with other AWS services, such as Amazon Comprehend, Amazon Polly, Amazon Lex, Amazon Transcribe, and Amazon S3. You can use these services to perform sentiment analysis, text-to-speech synthesis, conversational interfaces, transcription, and storage of translated content.
  • Provides a simple and secure API that you can access from any application or platform. You can use the API to translate text or speech on demand or stream audio data for continuous translation.

Use Cases for Amazon Translate

Some of the use cases for Amazon Translate are:

  • Customer service: Provide multilingual support to your customers via chatbots, email, phone, or social media. You can also use it to translate customer feedback and reviews into your preferred language.
  • E-commerce: Expand your global reach by offering your products and services in multiple languages. You can also use it to translate product descriptions, reviews, and user-generated content.
  • Education: Enhance your learning experience by accessing educational content in different languages. You can also use it to create multilingual courses and assessments for your students.
  • Media and entertainment: Create subtitles and captions for your videos and podcasts in different languages. You can also use it to translate news articles, blogs, and social media posts.
  • Travel and tourism: Communicate with travelers and locals in different languages. You can also use it to translate travel guides, brochures, menus, and signs.

Getting Started

To get started with Amazon Translate, you need to:

  • Sign up for an AWS account if you already have one.
  • Create an IAM user with the necessary permissions to access Amazon Translate.
  • Install the AWS SDK or CLI on your device or platform of choice.
  • Use the API or CLI commands to translate text or speech in real-time.

For more information on how to use Amazon Translate, please refer to the official documentation: https://docs.aws.amazon.com/translate/index.html.

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Amazon Polly: Your Guide to Voice-Enabled Magic

Amazon Polly: Your Guide to Voice-Enabled Magic

Voice is a powerful way to communicate with your audience. Whether you want to narrate a story, explain a concept, or provide feedback, voice can make your content more engaging and accessible. But how do you create high-quality voice content without hiring professional voice actors or spending hours recording and editing audio files? The answer is Amazon Polly.

Amazon Polly is a service that turns text into lifelike speech. You can use Amazon Polly to generate voice content for your website, app, podcast, video, or any other project that needs voice. Amazon Polly supports over 60 voices and 29 languages, so you can choose the voice that suits your brand and audience. You can also customize the voice output with features such as speech marks, SSML tags, and lexicons.

Benefits of using Amazon Polly

  • Create voice content faster and cheaper than hiring voice actors or recording audio.
  • Easily update your voice content by changing the text input without re-recording or editing the audio.
  • Reach more users by making your content accessible to people who prefer listening over reading or who have visual or reading impairments.
  • Enhance your user experience by adding voice interactivity and personalization to your content.

Use Cases

  • You can create audio versions of your blog posts, articles, ebooks, or newsletters to increase engagement and retention.
  • You can add voice feedback or guidance to your app or website to improve usability and navigation.
  • You can create podcasts or videos with voice narration to share your knowledge and expertise.
  • You can generate voice prompts or messages for your chatbot, IVR, or voice assistant to provide a natural and conversational interface.

Getting started with Amazon Polly is easy. You can access the service through the AWS console, CLI, or the AWS SDKs. You can also use the Amazon Polly WordPress plugin to convert your WordPress posts into podcasts. To learn more about Amazon Polly and how to use it, visit the official documentation here.

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Amazon Forecast: Precision in Time-Series Prediction

Amazon Forecast: Precision in Time-Series Prediction

Time-series forecasting is a challenging task that requires complex models and large datasets. However, with Amazon Forecast, you can simplify this process and get accurate predictions for your business needs. Amazon Forecast is a fully managed service that uses machine learning to generate forecasts based on historical data and other variables. You can use it for various use cases, such as demand planning, inventory optimization, resource allocation, and anomaly detection. This post will show you how to start with Amazon Forecast and leverage its features and benefits.

Getting Started

First, you must create a dataset group and import your historical data into Amazon Forecast. You can use the AWS console, the AWS CLI, or the AWS SDKs. You can also use built-in data connectors to import data from Amazon S3, Amazon Redshift, or Amazon Athena. Your data should include a timestamp column, a target value column, and any other relevant features that can influence your forecasts.

Next, you need to create a predictor and train a forecasting model. It will automatically select the best algorithm for your data and optimize its hyperparameters. You can also choose from a list of predefined algorithms or provide your custom algorithm. You can also specify how far ahead you want to forecast and how often you want to generate forecasts.

Finally, you need to create a forecast and query the results. You can use the AWS console, the AWS CLI, or the AWS SDKs. You can also use the Amazon Forecast Query API to programmatically access your forecasts. You can view various metrics and visualizations to evaluate the accuracy and quality of your forecasts. You can also export your forecasts to Amazon S3 or consume them in other AWS services.

What Amazon Forecast Offers

Amazon Forecast is a powerful tool that can help you make better decisions based on data-driven insights. You can benefit from:

  • High accuracy: Uses advanced machine-learning techniques to capture complex patterns and trends in your data.
  • Scalability: Handle large volumes of data and generate forecasts for millions of items.
  • Flexibility: Handle different types of data and forecasting scenarios, such as seasonal, intermittent, or irregular patterns.
  • Ease of use: Does not require machine learning expertise and provides a simple and intuitive interface.
  • Cost-effectiveness: Charges you only for what you use and offers a free tier for the first two months.

To learn more about Amazon Forecast, visit the official documentation or check out some of the sample notebooks and tutorials on GitHub. You can also try out the service for free with the AWS Free Tier. Start forecasting today with Amazon Forecast!

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AWS AI and ML Deployment: Security Best Practices

AWS AI and ML Deployment: Security Best Practices

This blog post will share some best practices for securing your AWS AI and ML deployment, covering features, benefits, use cases, and getting started.

As organizations increasingly harness the power of Artificial Intelligence (AI) and Machine Learning (ML) to drive innovation and gain competitive advantages, ensuring the security of AI and ML deployments becomes paramount. As a leading cloud provider, AWS offers a robust suite of services for AI and ML, but safeguarding these deployments against evolving threats is a multifaceted challenge.

AWS AI and ML services enable you to build, train, and deploy intelligent applications quickly and easily. However, you must also ensure that your AI and ML deployments are secure and compliant with your organization’s policies and standards.

Features and Benefits of AWS AI and ML Deployment Security

AWS AI and ML deployment services provide several features and benefits that help you secure your deployments, such as:

  • Encryption: You can encrypt your data at rest and in transit using AWS Key Management Service (KMS) or your own encryption keys. You can also use AWS Certificate Manager (ACM) to manage SSL/TLS certificates for your endpoints.
  • Identity and Access Management (IAM): You can use IAM to control who can access your AWS AI and ML resources and what actions they can perform. You can also use IAM roles to grant your applications or users temporary permissions.
  • Audit and Compliance: AWS CloudTrail can monitor and log all API calls made by or on behalf of your AWS AI and ML services. You can also use AWS Config to track the configuration changes of your resources and AWS Security Hub to view security findings and alerts across your AWS accounts.
  • Firewall and Network Protection: AWS WAF protects your web applications from common web attacks, such as SQL injection and cross-site scripting. You can also use AWS Shield to protect your applications from distributed denial-of-service (DDoS) attacks and AWS VPC to isolate your network resources.

Use Cases for AWS AI and ML Deployment Security

Some common use cases for securing your AWS AI and ML deployments are:

  • Data Privacy: You can use encryption, IAM, and firewall features to protect the privacy of your data from unauthorized access or leakage. For example, you can use Amazon SageMaker to train and deploy machine learning models with encrypted data and endpoints, or use Amazon Comprehend Medical to analyze health data with HIPAA compliance.
  • Fraud Detection: You can use audit and compliance features to detect and prevent fraud or abuse of your AI and ML applications. For example, you can use Amazon Fraud Detector to create custom fraud detection models with CloudTrail integration or use Amazon Rekognition to verify the identity of your users with facial recognition.
  • Threat Detection: You can use firewall and network protection features to detect and mitigate threats to your AI and ML applications. For example, you can use Amazon GuardDuty to monitor your AWS accounts for malicious activity or use Amazon Macie to discover and protect sensitive data in S3 buckets.

Getting Started with AWS AI and ML Security

To get started with securing your AWS AI and ML deployments, you can follow these steps:

  • Review the security best practices for each AWS AI and ML service you use or plan to use. You can find the security documentation for each service on the AWS website.
  • Enable encryption, IAM, CloudTrail, Config, Security Hub, WAF, Shield, and VPC for your AWS AI and ML resources. To configure these features, you can use the AWS Console, CLI, SDKs, or CloudFormation templates.
  • Test Regularly monitor your AWS AI and ML deployments for security issues. You can use tools like Amazon Inspector, Amazon CodeGuru, or Amazon DevOps Guru to scan your code and infrastructure for vulnerability or performance issues.

Conclusion

Securing your AWS AI and ML deployments is essential for ensuring the trustworthiness and reliability of your intelligent applications. By following the best practices outlined in this blog post, you can leverage the features and benefits of AWS AI and ML services to build secure and compliant solutions for your business needs.

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Azure LUIS: Mastering Language Understanding

Azure LUIS: Mastering Language Understanding

Overview

Azure LUIS is a cloud-based service that enables you to build natural language understanding into your applications. With Azure LUIS, you can create custom models that recognize the intents and entities of your users’ queries and commands. You can also use pre-built models for common scenarios such as booking, calendar, email, etc.

Benefits of Azure LUIS

Some of the benefits are, you can:

  • Easily integrate it with other Azure services such as Bot Framework, Cognitive Services, and Speech Services.
  • Create and manage your models using a graphical interface or a REST API.
  • Train and test your models with real data and feedback.
  • Deploy your models to multiple regions and scale them as needed.
  • Monitor and improve your models with analytics and suggestions.

Use Cases for Azure LUIS

Some of the use cases are:

  • Chatbots and virtual assistants that can handle natural language conversations with your customers or employees.
  • Voice-enabled applications that can understand spoken commands and queries.
  • Search engines and knowledge bases that can provide relevant results based on natural language queries.
  • Text analysis and extraction that can identify key information and insights from unstructured text.

Getting Started

To get started, you need to:

  • Create an Azure account and a LUIS resource in the Azure portal.
  • Create a LUIS app and define your intents and entities in the LUIS portal or using the REST API.
  • Train your app with example utterances and label them with intents and entities.
  • Test your app using the LUIS portal or the REST API.
  • Publish your app to an endpoint and integrate it with your application.

For more information, visit the official documentation: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/.

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Azure Text Analytics for Customer Feedback Sentiment

Azure Text Analytics for Customer Feedback Sentiment

In this blog post, we will show you how to use Azure Text Analytics for sentiment analysis of customer feedback.

Overview

Customer feedback is a valuable source of information for any business. It can help you understand your customers’ needs, preferences, and satisfaction levels. However, analyzing customer feedback manually can be time-consuming and prone to errors. That’s why you need a tool to automate the process and provide insights in minutes.

Azure Text Analytics is a cloud-based service that offers natural language processing capabilities, such as sentiment analysis, keyphrase extraction, language detection, and more. You can easily analyze customer feedback from various sources, such as surveys, reviews, social media, and emails. You can integrate it with other Azure services like Power BI, Logic Apps, and Cognitive Search.

What is Sentiment Analysis and Why is it Important?

Sentiment analysis is identifying and extracting a text’s emotional tone and attitude. It can help you measure customers’ feelings about your products, services, or brand. For example, you can use sentiment analysis to:

  • Monitor customer satisfaction and loyalty.
  • Identify customer pain points and areas of improvement.
  • Detect customer complaints and issues.
  • Discover customer advocates and influencers.
  • Enhance customer experience and retention.

Sentiment analysis can also help you gain a competitive advantage by understanding how your customers perceive your competitors. You can use this information to improve your marketing strategies, product development, and customer service.

Features and Benefits of Azure Text Analytics for Sentiment Analysis

Azure Text Analytics for sentiment analysis provides you with the following features and benefits:

  • Accurate and reliable sentiment scores: It uses advanced machine learning models to assign a sentiment score to each text. The score ranges from 0 (negative) to 1 (positive), with 0.5 being neutral. You can also get sentiment scores at the document, sentence, or aspect level.
  • Multilingual support: Supports over 20 languages for sentiment analysis, including English, Spanish, French, German, Chinese, Japanese, and more. You can also detect the language of the text automatically.
  • Customizable models: You can customize the sentiment models to suit your domain or industry. You can use the Custom Text feature to train your models with your data and labels.
  • Scalable and secure service: It can handle large volumes of text with high performance and availability. You can also rest assured that your data is secure and compliant with Azure’s privacy and security standards.

How to Get Started with Azure Text Analytics for Sentiment Analysis?

Getting started with Text Analytics for sentiment analysis is easy and fast. Here are the steps you need to follow:

  1. Create an Azure account: If you don’t yet have one, you can create one for free here.
  2. Create a Cognitive Services resource: To use Azure Text Analytics, you must create a Cognitive Services resource in your Azure portal. You can follow this tutorial to learn how.
  3. Get your endpoint and key: Once you create your Cognitive Services resource, you will get an endpoint URL and a subscription key that you will need to access the service.
  4. Choose your preferred method: You can use Azure Text Analytics for sentiment analysis in different ways, such as:
    • Web interface: Text Analytics demo page to test the service with your own or sample text.
    • REST API: Text Analytics REST API to send HTTP requests and get JSON responses. You can follow this tutorial to learn how.
    • SDKs: Text Analytics SDKs to integrate the service with your preferred programming languages, such as Python, C#, Java, or JavaScript. You can follow this tutorial to learn how.
    • Connectors: Text Analytics connectors to integrate the service with other Azure services or third-party applications, such as Power BI, Logic Apps, or Cognitive Search. You can follow this tutorial to learn how.

We hope this blog post has given you an overview of how to use Azure Text Analytics for sentiment analysis of customer feedback. You can visit the official documentation page here to learn more about Azure Text Analytics. If you have any questions or feedback, please comment below or contact us here.

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Plugins for Azure OpenAI Service: How-to Guide

Plugins for Azure OpenAI Service: How-to Guide

Use Plugins for Azure OpenAI Service

In this blog post, we will explore different types of plugins for Azure OpenAI Service and how to use them for your projects.

Azure OpenAI Service is a cloud-based platform that lets you access the powerful capabilities of OpenAI models. You can create natural language applications, generate text and images, and analyze data. But did you know that you can also extend the functionality of Azure OpenAI Service with plugins?

Plugins are optional components you can install on your Azure OpenAI Service account. They allow you to customize the behavior of the OpenAI models, add new features, and integrate with other services. In this blog post, we will explore the different types of plugins available for Azure OpenAI Service and how to use them for your projects.

Types of Plugins for Azure OpenAI Service

There are three main types of plugins: model plugins, feature plugins, and integration plugins.

Model plugins let you modify the parameters and settings of the OpenAI models. For example, you can use a model plugin to change the text generation model’s temperature, frequency, or presence penalty. You can also use a model plugin to fine-tune the model on your data or add custom tokens.

Feature plugins let you add new capabilities to the OpenAI models. For example, a feature plugin can enable sentiment analysis, summarization, or translation for the natural language model. You can also use a feature plugin to generate different images, such as faces, logos, or landscapes.

Integration plugins let you connect the OpenAI models with other services and platforms. For example, you can use an integration plugin to send and receive data from Microsoft Power BI, Google Sheets, or Slack. You can also use an integration plugin to deploy your applications on Azure App Service, Azure Functions, or Azure Kubernetes Service.

How to Use Plugins for Azure OpenAI Service

To use Azure OpenAI Service plugins, you need an active account and a subscription plan. You can sign up for a free trial or choose from one of the paid plans on the Azure portal.

Once you have an account and a subscription plan, you can browse and install plugins from the Azure Marketplace. The Azure Marketplace is an online store where you can find and buy software and services from Microsoft and third-party providers.

To Install Plugins

  1. Go to the Azure portal and click Create a resource.
  2. Search for OpenAI and choose OpenAI Service.
  3. In the next page, click Plugins to see a list of available plugins for Azure OpenAI Service.
  4. Click the required plugin name and click Get it now.
  5. You must agree to the terms and conditions and provide some basic information.
  6. Click Create and wait for the installation to complete.

Using the Dashboard

After installing a plugin, you can use it from the Azure OpenAI Service dashboard. The dashboard allows you to manage your account, create projects, and access the OpenAI models.

  1. To open the dashboard, go to the Azure portal and click OpenAI Service under All resources.
  2. In the dashboard, you will see a tab for each plugin you have installed.
  3. Click a tab to open the plugin interface and start using it. Depending on the type of plugin, you may need to provide some inputs, such as text, images, or data sources.
  4. Then, click Run or Generate to see the output from the OpenAI model.
  5. You can also use plugins from code by using the Azure OpenAI Service SDK. The SDK library lets you interact with the OpenAI models programmatically. You can use it with Python, C#, Java, or Node.js.

Using Plugin from Code

To use plugins from code, you need to import the SDK and initialize an instance of the OpenAI client. Then, you need to specify the name of the plugin you want to use and pass it as an argument to the corresponding method. If you want to use OpenAI for sentiment analysis, you can use the OpenAI API. Here’s an example:

import openai
openai.api_key = 'your-api-key'
response = openai.Completion.create(
  engine="text-davinci-002",
  prompt="Sentiment analysis of the following text:\nI love Azure OpenAI Service!\n",
  temperature=0.5,
  max_tokens=1
)
print(response.choices[0].text.strip())

Replace ‘your-api-key’ with your actual OpenAI API key. This script will return the sentiment of the text as a string (e.g., “Positive”, “Negative”, or “Neutral”).

Conclusion

Azure OpenAI Service is a powerful platform that lets you leverage the capabilities of OpenAI models for your projects. With plugins, you can further enhance and customize your experience with Azure OpenAI Service. You can choose from various plugins that suit your needs and goals.

To start with Azure OpenAI Service plugin, sign up for a free trial or choose a subscription plan on the Azure portal. Then, browse and install plugins from the Azure Marketplace and use them from the dashboard or code.

We hope this blog post has given you an overview of the different types of plugins for Azure OpenAI Service and how to use them. Please let us know in the comments below if you have any questions or feedback. Happy coding!

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