Category: Cloud Ops

What is Azure Document Intelligence?

Azure Document Intelligence, formerly known as Azure Form Recognizer, is a cloud-based service provided by Microsoft Azure that uses machine learning to extract information from documents. It is designed to automate the process of extracting data from various types of documents, such as invoices, receipts, business cards, and more. Here are some key features and benefits of Azure Document Intelligence:

Key Features

• Pre-Built Models: Ready-to-use models for common document types like invoices, receipts, and business cards.
• Custom Models: Train custom models tailored to your specific document types and formats.
• Layout Extraction: Extracts text, tables, and structure from documents, preserving the layout and formatting.
• Key-Value Pair Extraction: Identifies and extracts key-value pairs from documents.
• Table Extraction: Extracts tables from documents, preserving the rows and columns for easy data manipulation.

Benefits

• Automation: Reduces manual data entry and processing time by automating the extraction of information from documents.
• Scalability: Scales with your business needs, allowing you to process thousands of documents quickly and efficiently.
• Integration: Easily integrates with other Azure services and third-party applications through APIs.
• Cost-Effective: Offers flexible pricing models, including pay-as-you-go and commitment tiers, to suit different business needs and budgets.
• Security: Ensures data privacy and security with robust compliance and security measures in place.

Use Cases

• Financial Services: Automate the extraction of data from invoices, receipts, and financial statements.
• Healthcare: Extract information from medical forms, patient records, and insurance documents.
• Retail: Process receipts, purchase orders, and inventory documents.
• Government: Automate the handling of applications, permits, and identity documents.
• Education: Extract data from student records, transcripts, and application forms.

Document Intelligence Pricing Tiers

Azure Document Intelligence offers various pricing tiers to cater to different needs and use cases. In this blog, we’ll explore the different tiers available, focusing on their features, benefits, and ideal use cases.

1. Pay-As-You-Go

The Pay-As-You-Go model provides flexibility and is ideal for users who prefer to pay based on their actual usage. This model includes two sub-tiers:

Free Tier

• Description: Provides limited access to Azure Document Intelligence services at no cost.
• Benefits: Cost-Free: Ideal for small-scale projects, testing, and development. Basic Features: Access to essential features to get started with Azure Document Intelligence.
• Use Cases: Suitable for individuals, small businesses, and developers who want to explore Azure Document Intelligence without any financial commitment.

S0 Tier

• Description: A standard pay-as-you-go tier that offers more features and higher usage limits compared to the Free tier.
• Benefits: Scalable: Pay only for what you use, with no upfront costs. Enhanced Features: Access to a broader range of features and higher usage limits.
• Use Cases: Ideal for businesses and applications that require more extensive use of Azure Document Intelligence services but prefer a flexible, usage-based pricing model.

2. Commitment Tier

The Commitment Tier is designed to provide cost-effective access to a variety of AI services, including OpenAI. This model includes three sub-tiers:

Connected Container

• Description: Allows you to run AI services in a container that maintains a connection to Azure.
• Benefits: Real-Time Updates: Continuous access to the latest updates and features from Azure. Seamless Integration: Easy integration with other Azure services and resources. Scalability: Leverage Azure’s infrastructure to scale your AI services as needed.
• Use Cases: Ideal for environments where constant internet connectivity is available and desired, such as cloud-native applications and services that require real-time data processing.

Disconnected Container

• Description: Allows you to run AI services in a container without requiring a constant connection to Azure.
• Benefits: Offline Capability: Operate in environments with limited or no internet connectivity. Security: Enhanced security for sensitive data by reducing the need for external connections. Flexibility: Suitable for edge computing scenarios where data needs to be processed locally.
• Use Cases: Perfect for remote locations, industrial IoT applications, and scenarios where data privacy and security are paramount.

Azure Standard

• Description: Provides a balanced approach between cost and service availability.
• Benefits: Cost-Effective: More affordable access to a wide range of AI services compared to pay-as-you-go models. Comprehensive Access: Includes access to various AI services, including OpenAI, machine learning models, and cognitive services. Support: Standard support from Azure, ensuring reliability and assistance when needed.
• Use Cases: Suitable for most organizations looking to leverage AI capabilities without incurring high costs, including businesses, educational institutions, and research organizations.

Conclusion

Choosing the right pricing tier for Azure Document Intelligence depends on your specific needs and use cases. The Pay-As-You-Go model offers flexibility with its Free and S0 tiers, making it ideal for small-scale projects and businesses that prefer a usage-based pricing model. On the other hand, the Commitment Tier provides cost-effective access to a wide range of AI services, with options for connected and disconnected containers, as well as a standard tier for comprehensive access.

By understanding these tiers, you can make an informed decision that best suits your requirements. For more detailed information, you can refer to the Azure AI Services Commitment Tier documentation https://learn.microsoft.com/en-us/azure/ai-services/commitment-tier

Platform engineering refers to the discipline focused on designing, managing, and optimizing the platforms developers use to build, deploy, and manage applications. Let’s dive into what this discipline encompasses:

IaC: Infrastructure configuration is developed by platform engineers in the same way as a software code. Tools like Terraform, Ansible,Bicep or AWS CloudFormation are used to describe and provision infrastructure resources in a repeatable and version-controlled method.

Containerization and Orchestration: Platform engineering ensures, through technologies like Docker for containerization and Kubernetes for orchestration, that applications are running consistently in different environments and scaled efficiently.

Continuous Integration/Continuous Deployment Pipelines: Automatically build, test, and deploy applications, enabling faster and more reliable release of an application through Continuous Integration and Continuous Deployment/Continuous Delivery systems. A good example is Jenkins, GitLab CI, and GitHub Actions.

Generally, this term applies to the establishment of monitoring systems for application performance, infrastructure health, and user experience. The most common tools for gaining insights and remediating problems are Prometheus, Grafana, and the ELK stack.

Security Integration: Baking security into the platform from the outset, popularly referred to as DevSecOps, means imbibing security practices right across the development lifecycle.

API Management: Define, publish and maintain APIs to enable proper and seamless communication among various parts of the application or diverse applications.

Scalability and Performance: Making sure the platform can support an increasing load without compromising performance – for instance, through load balancing, auto-scaling, or optimization of resource utility.

Cost Management: Efficient management of cloud or on-premise resources is critical to minimizing expenses while maximizing utility. This is often accomplished through automation and intelligent resource allocation.

Developer Experience (DX): There is a strong emphasis on making the platform intuitive and self-service, reducing the need for manual intervention. The goal is to enable developers to focus on writing code without worrying about underlying infrastructure.

Why Platform Engineering is Important?

Platform engineering plays a key role in enabling software teams to innovate quickly and reliably. By creating an environment where developers can focus solely on writing code, platform engineers remove the complexities of managing the infrastructure beneath. This field is constantly evolving, with new tools, methodologies, and practices emerging to enhance how software is taken from ideation to production

In the rapidly evolving world of DevOps, managing infrastructure can often become a complex and time-consuming task. Azure DevOps is stepping up to address these challenges with a groundbreaking feature: Managed DevOps Pools (MDP). This innovative solution is designed to streamline the creation and management of custom DevOps agent pools, offering a host of benefits that can transform the way your team handles DevOps workflows.

What is Managed DevOps Pools (MDP)?

Managed DevOps Pools (MDP) is a new feature within Azure DevOps that provides a fully managed approach to creating and overseeing custom DevOps agent pools. This means you get the benefits of custom agent pools without the associated management overhead. Here’s a closer look at the key highlights:

1. Host on Behalf Model

MDP operates on a “host on behalf” model, where Azure takes on the responsibility of creating and managing the virtual machines (VMs) that power your DevOps agents. These VMs are hosted in Microsoft-owned Azure subscriptions, significantly reducing the management burden on your team. This model ensures that your infrastructure is maintained by Azure while you focus on more critical tasks.

2. Time Savings

One of the standout benefits of MDP is the substantial reduction in time spent managing and troubleshooting agent infrastructure. Compared to self-hosted or Azure VM Scale Set agents, MDP drastically cuts down the time required for infrastructure management. This allows your team to dedicate more time to development and less time to operational concerns.

3. Specific Pools for Specific Needs

With MDP, creating team-specific or workload-specific pools is a breeze. This flexibility means you can optimize performance and cost by tailoring pools to meet the unique needs of different teams or projects. Whether you need specialized environments for different stages of your pipeline or dedicated resources for high-priority tasks, MDP has you covered.

4. Cost Optimization

MDP is designed with cost efficiency in mind. Features like Standby Agents help manage and optimize your DevOps costs, potentially leading to significant savings. Some customers have reported up to a 50% reduction in Azure billing as a result of using MDP. By leveraging these cost-saving features, you can ensure that your DevOps investments are as efficient as possible.

5. Scalability

Scalability is another key advantage of MDP. It can handle thousands of agents within a single pool, making it well-suited to manage bursty traffic and reduce pipeline queuing. Whether you’re dealing with sudden spikes in demand or scaling down for routine operations, MDP provides the flexibility and capacity you need to keep your pipelines running smoothly.

6. Flexible Configurations

MDP offers a range of flexible configuration options to match your specific workload requirements. You can use quick-starter images for fast setup, connect to private networks, bring in your own custom images, and select the appropriate Azure SKU and additional storage. This level of customization ensures that your DevOps environment is perfectly aligned with your operational needs.

Overall, Managed DevOps Pools (MDP) offer a hybrid advantage: the adaptability of self-hosted agents with the maintenance simplicity of Microsoft-hosted agents. MDP reduces management tasks, optimizes expenses, and delivers scalable, adaptable configurations, enabling your team to concentrate on developing applications and services instead of overseeing infrastructure. Adopt MDP to enhance your DevOps workflows and elevate your team’s productivity.

To discover more about Managed DevOps Pools and how to begin using the service, please visit Managed DevOps Pools page.

Azure API Management just got a major upgrade with the General Availability of Workspaces! This feature is set to revolutionize how teams manage, create, and publish APIs.

Why Workspaces Matter:

• Autonomy for API Teams: Each team can now own their API lifecycle, from development to deployment, in their isolated workspace.
• Centralized Governance: While teams work independently, the API platform team maintains oversight, ensuring governance and consistency across the board.
• Enhanced Security and Reliability: With isolated environments, teams can experiment and innovate without risking the stability of the broader API ecosystem.
• Federated API Management: This model allows for better scalability, where multiple teams can operate simultaneously with less overhead.

Key Benefits:

• Productivity Boost: Teams can work faster with less coordination overhead.
• Security: Isolated runtimes mean safer testing and development environments.
• Innovation: Teams can experiment more freely, leading to faster innovation cycles.

How to Get Started:

1. Dive into the Azure portal to create your first workspace.
2. Assign roles like API Management Workspace API Developer or Product Manager to your team members.
3. Start managing your APIs with the confidence of having your own sandbox.

This update from Azure isn’t just an enhancement; it’s a game-changer for how organizations approach API management. Ready to elevate your API game? Check out the full details and get started!

Announcing General Availability of Workspaces in Azure API Management – Microsoft Community Hub