Microsoft offers a variety of technologies and platforms for developing generative AI solutions, enabling organizations to harness the power of AI to create innovative applications and services. There are four technologies that can be adopted by the developer
Azure Open AI Services provides access to advanced AI models, including GPT-4, through easy-to-use APIs. This democratizes AI development by allowing developers of any skill level to integrate AI functionality into their applications without needing specialized knowledge or hardware investments
Azure AI Studio is a unified environment for building, training, and deploying AI models. It offers tools for data preparation, model training, and deployment, making it easier for developers to create and manage AI solutions.
The Copilot Ecosystem includes tools and services that enhance productivity and creativity by leveraging generative AI. It provides capabilities for generating ideas, designing personalized marketing campaigns, and more
The Azure AI Model Catalog offers a collection of pre-trained models that developers can use to jump-start their AI projects. These models cover a wide range of tasks, from language translation to image recognition, providing a solid foundation for building custom solutions.
You can use Visual Studio and Azure By leveraging these technologies and platforms, developers can create powerful generative AI solutions that drive innovation and efficiency across various industries. You can learn more about Generative AI here
Auto-scaling is a feature that dynamically adjusts the amount of compute and storage resources allocated to your Azure SQL Database based on current workloads. This capability ensures that your database can handle varying loads efficiently, providing better performance and cost management. Auto-scaling in Azure SQL Database is not a native feature, but it can be implemented using Azure's powerful automation and scaling capabilities. The process involves monitoring your database's performance metrics and setting up rules that trigger scaling actions when certain thresholds are reached.
Setting Up Auto-Scaling
To set up auto-scaling for your Azure SQL Database, you'll need to follow these general steps:
Utilize Azure's monitoring tools to keep an eye on your database's performance. Pay special attention to metrics like CPU usage, memory consumption, and I/O rates.
Azure Automation allows you to create runbooks, which are collections of scripts that automate cloud management tasks. For auto-scaling, you'll write a PowerShell script that changes the performance level of your database when specific conditions are met.
Decide on the conditions that will trigger a scale-up or scale-down. For example, you might want to scale up when CPU usage exceeds 80% for a sustained period.
Azure Logic Apps can be used to schedule and orchestrate the scaling actions based on the rules you've defined. They can call the runbooks you've created in Azure Automation to perform the scaling.
Before relying on auto-scaling in a production environment, thoroughly test your setup to ensure it behaves as expected. Monitor the results and refine your scripts and rules as necessary.
Considerations and Best Practices
Azure SQL Database offers different performance tiers and service levels. Ensure that your auto-scaling setup considers the capabilities and limits of your chosen tier.
While auto-scaling can help manage costs by reducing resources during low demand, it's important to monitor your spending to avoid unexpected charges.
Consider how auto-scaling interacts with your database's failover and high availability setup. Ensure that scaling actions do not compromise the resilience of your system.
For certain scenarios, Azure SQL Database's serverless tier can automatically scale compute resources, providing a simpler alternative to custom auto-scaling solutions.
Conclusion
Auto-scaling is a powerful technique to optimize the performance and cost of your Azure SQL Database. By leveraging Azure Automation, Logic Apps, and careful monitoring, you can create a responsive and efficient database environment that scales with your needs. For detailed instructions and script examples, refer to the comprehensive tutorial provided by the Microsoft Community Hub, and explore other resources that offer insights into auto-scaling strategies and best practices. Remember, while auto-scaling can greatly enhance your database's efficiency, it requires careful planning and continuous monitoring to ensure optimal results.
.NET Development and AI: Harnessing the Power of C# and Microsoft's AI Platform
The intersection of .NET development and artificial intelligence (AI) has become an increasingly significant area of focus for software engineers and researchers alike. With the rise of advanced AI technologies. One of the trends that we have witnessed in recent years is the seamless integration of C# programming language and Microsoft's AI platform, enabling developers to create innovative software solutions that leverage the power of both.
Software developer today will have several scenarios that can be used to create software that has built in AI. Some of the scenario that commons are:
Adding conversation between your application and AI. For example, you put an AI rewrite feature in the application or have a dialog between your application and AI. On this scenario, we can use Azure OpenAI
Adding predictive maintenance capabilities to your application to forecast when equipment might fail, or sales will rise. On this scenario, we can use ML.NET Model Builder.
Developing chatbot or virtual assistant that can understand and respond to user queries. You can use Azure Open AI, and The Semantic kernel SDK to make it happen.
If you want to build one of the solution you can visit this Collections | Microsoft Learn to make you accelerate the development
When you want to create a solution that uses natural language processing (NLP), you can use a lot of open-source libraries. However, if you take a closer look at the Azure AI, they have NLP features through Azure AI language, and it starts from FREE.
Natural Language Processing (NLP) development with Azure AI involves utilizing Microsoft's suite of tools and services to build, deploy, and manage NLP models and applications. Azure offers a range of NLP-related services such as Azure Cognitive Services, Azure Machine Learning, and Azure Databricks, which provide capabilities for language understanding, sentiment analysis, named entity recognition, and more.
Using Azure AI for NLP development allows developers to harness the power of pre-built models and APIs for common NLP tasks, as well as the flexibility to build custom NLP models using machine learning frameworks like TensorFlow and PyTorch on Azure Machine Learning. Additionally, Azure provides infrastructure and tools for data processing, model training, and deployment, making it a comprehensive platform for NLP development.
By leveraging Azure AI for NLP development, businesses and developers can expedite the creation of language-aware applications, automate text analysis workflows, and gain insights from unstructured data sources. Azure's robust security and compliance features also ensure that NLP applications built on the platform adhere to industry standards and best practices. Overall, Azure AI empowers developers to create sophisticated NLP solutions while benefiting from the scalability, reliability, and performance of the Azure cloud platform.
To create an Azure AI Language project using Visual Studio, follow these steps:
Provision Azure Resources:
Create an Azure Subscription (you can create one for free).
Log into Language Studio.
If it’s your first time logging in, choose a language resource and select “Create a new language resource.” Provide details such as name, location, and resource group.
Use Language Studio with Your Own Text:
Once you’re ready to use Language Studio features on your text data, you’ll need an Azure AI-language resource for authentication and billing. I recommend you do not need to activate this because it needs to be paid, but of course, I recommend you to subscribe when the transaction goes up.
Follow the setup process to create your resource.
You can then call REST APIs and use client libraries programmatically. You can see a lot examples here Language Studio - Microsoft Azure
Remember to choose a location for your Azure AI language resources so the latency of the resources
Some NLP scenarios that you can expect:
Extract information that comes from the document/text. For example, you want to understand the main topic or contribution of an article
Classify text for sentiment analysis, language detection, and custom text classification. For example, you want to moderate content in the forum
Question and answer. For example, creating a Bot for simple question-and-answer.
Summarize information. For example, you want to create meeting notes based on the meeting documents / conversational text
Customize translation. For example, you want to create a translation of a natural language to cat language :D
In this article, we will discuss how to step through a multi-account Azure Environment for Cost Optimization. An organization might need to restructure its multi-accounts. Here is why.
Multi-Account means that the account belongs to each of the responsible roles. This is good when Azure's budget is separated between divisions.
Multi-Account means we can open as many subscriptions as we need. For example, one subscription is for Pay As You Go, one is for DevTest, and others are for Collaboration.
Multi-account means we can consolidate using manage organizations and have separate Microsoft Entra IDs. This is great for companies that build in Microsoft Azure for their customers. So any development won't mess up their Entra ID.
So here are the steps.
Create management groups. Manage your Azure subscriptions at scale with management groups - Azure Governance - Azure governance | Microsoft Learn
Structure your subscriptions, for example,
Research subscription - for research purposes.
Development Subscription - for development purposes
Your customer subscription—managed services for the production server. I recommend you create one subscription for customers with a huge workload.
Product subscription—This is an internal system in the organization, such as a website, internal information system, DevOps subscriptions, and many more.
Structure the organization by managing group roles Organize subscriptions into management groups and assign roles to users - Microsoft Defender for Cloud | Microsoft Learn
Create resource groups based on the Project. Manage resource groups - Azure portal - Azure Resource Manager | Microsoft Learn
Managing tags. The organization should have tags to consolidate tags for any activities such as "safe to delete" "production" "expired" "high transaction" etc. Tag resources, resource groups, and subscriptions with Azure portal - Azure Resource Manager | Microsoft Learn
This article is going with post for Azure Global Bootcamp 2023 that held in Cilacap Indonesia. On this session, I shared about how to use AI in Visual Studio 2022 and Visual Studio Codes, you can grab and see the decks on this post.
If you want to join my live session, you can join at https://bit.ly/globalazure2023 , see you there
Problem
In the past, we move the azure resource through three ways.
Snapshot and copy
Scripting through PowerShell's
Redeploy the solution
It has a lot of activities and manual actions. If you have a lot
Solution
Today, we have a new way to deploy the azure resources namely Azure Resource Mover. You can watch the video here
Problem
Our customer has an issue with their cloud computing expense. They have allocated a VM with over specification with their budget. This is because the VM will work hard in specific time. The customer uses the VM in 09.00 AM and 5 PM. After that the VM is not used.
Requirements
The customer wants you to reduce the cost by downscaling and upscaling the VM based on the time.
Workdays: 09.00 – 17.00 PM
Weekend: Off
//
Solution
You have several solutions to accommodate this.
Turn off the VM outside the workdays and turn of the VM in the workdays (Option A)
Scale down the VM in non-workday, and scale up in the workday (Option B)
Well-Architecture Recommendation
Choosing A, we will get better cost efficiency. However, there is some circumstance when the VM is misbehaving because the cold-start issue.
Choosing B, we still get cost-saving. However, we still pay the downsizing cost
How to do that
We can use many ways to do that. In Azure, you can use Azure Automation. You can read here
Azure Automation Start/Stop VMs during off-hours overview | Microsoft Docs
Azure Automation: Scale-Down VM Size - Microsoft Tech Community
Auto Scale Up and Down VM's with Azure Automation (wordpress.com)
Background
Your customer has a VM (Virtual Machine). The VM contains of Windows Server 2019, SQL Server 2019 Web Edition, and ERP Software. The VM runs on Azure with no redundancy option activated. After a year, the VM has a problem to facilitate the request from the client. Scale up is the first think that we already done. However, the scalability is not fulfilled since one VM means one single point of failure. Therefore, we will make this single VM can be available for scalability purposes. This article will discuss how to prepare scalability environment for VM with 'least effort'
medianet_width = "600";
medianet_height = "250";
medianet_crid = "858385152";
medianet_versionId = "3111299";
Solution
After reading Make all things redundant - Azure Application Architecture Guide | Microsoft Docs, we strongly believe that we need load balancer on our VM and create a snapshot of our VM. On our case, we are very unlucky since the database and the web application is on one VM. Therefore, we need to do some extra steps as follows
Separating between database server and application server. If you are insisting to still make one VM, you will have a risk to lose your data. Snapshot a VM anytime when your database changes is not a good idea. The options are
VM + VM SQL Azure – this is the cheapest one, but you should manage the security and patching by yourself
VM + SQL Azure – if your application cannot be moved to App Service, this is win-win solution
App Service + SQL Azure – this is the most preferred way. Just Be aware of the cost
Replicate the database in multi-region you can do by visiting here
Creating multiple VM with scalability set. On this step, you should create snapshot of your VM and put the VM behind the load balancer. You can do that by reading this tutorial. You can create with snapshot and ARM template to do that.
If you need more than one region. For example, your customer is Europe while your base is in Asia. You can create multi-region VM. You need put the VM behind the Traffic manager. You can read the architecture recommendation here . You should deploy the VM on multiple regions plus you need to maintain the VM by creating regularly snapshot.
Multi-Region in regional pairing. I recommend this one, if your customer still on the same geographical area. Doing regional pair, will make sure that the recovery will be prioritized.
Multi-Region. I recommend this approach if your customer is separated geographically.
Do you have a problem in manage your scalable solution in VM, let's talk!
medianet_width = "600";
medianet_height = "250";
medianet_crid = "858385152";
medianet_versionId = "3111299";
Becoming architect in today computing is no longer easy just like before. In the past, we just need to learn a computer, its architecture, and its software. Today, business uses IT like never before. Virtualization, Client Server, Distributed systems, and of course cloud computing. On this article, we will discuss what we need to learn as professional architect
Professional Architect in Certification
In professional world, certification is a measurement of how career development goes. Therefore, let see what the expectation of certification in professional architect
Azure Solutions Architect
The professional architect should understand
Implement and monitor an Azure infrastructure
Implement management and security solutions
Implement solutions for apps
Implement and manage data platforms
Design monitoring
Design identity and security
Design data storage
Design business continuity
Design infrastructure
AWS Professional Architect
The professional architect on AWS should understand
Design and deploy dynamically scalable, highly available, fault-tolerant, and reliable applications on AWS
Select appropriate AWS services to design and deploy an application based on given requirements
Migrate complex, multi-tier applications on AWS
Design and deploy enterprise-wide scalable operations on AWS
Implement cost-control strategies
Learning as Architect
You can learn to become architect by joining digital training such as:
Microsoft Certified: Azure Solutions Architect Expert - Learn | Microsoft Docs
Exam Readiness: AWS Certified Solutions Architect – Professional | AWS Training & Certification
The Real Architect
You should learn by doing, so lab is better
You should learn by reading a lot of material
You should practices problem – solution