Learning Azure Fundamentals and Beyond

Azure is a cloud computing platform that offers a wide range of services, including computing, storage, networking, and artificial intelligence.

To get started with Azure, it's essential to understand the basics of cloud computing and the benefits of using a cloud platform. Azure provides a free account option, allowing you to try out its services without committing to a paid subscription.

Azure's learning path is designed to help you progress from beginner to expert. You can start by taking the Azure Fundamentals certification exam, which covers the core concepts of cloud computing and Azure services.

The Azure Fundamentals certification is a great starting point, but it's just the beginning. As you progress, you can move on to more advanced certifications, such as the Azure Developer Associate or Azure Architect Technologies certifications.

To get started with Azure, start with the basics. You can begin with Azure Fundamentals, a six-part series that explores basic cloud concepts.

This series will give you a streamlined overview of Azure services and practical exercises to deploy your very first services at no cost.

Azure Fundamentals is a great place to start your learning journey. It's a six-part series that explores basic cloud concepts.

You can get a streamlined overview of Azure services and practical exercises to deploy your very first services at no cost. This is a fantastic way to get hands-on experience and see the value of Azure for yourself.

Getting started with Azure Fundamentals is a great way to learn the basics of cloud concepts. You can start with a six-part series that explores these concepts in a streamlined and practical way.

Azure Fundamentals is a great place to start because it provides a comprehensive overview of Azure services. You can learn about the various services and how they can be deployed at no cost.

To get a visual roadmap of Azure certifications and their intersection with core skill areas and roles, you can explore an infographic. This will give you a quick overview of certifications and help you decide which ones are the best fit for your role and skill set.

Microsoft Azure Virtual Network (MAN) is a powerful and scalable cloud-based solution that allows you to create and manage your private network. It gives you the flexibility to connect any number of devices, applications, or servers without affecting the public internet.

Azure Data Factory is a platform that enables you to easily and rapidly create, deploy, and manage data-driven applications. It provides a comprehensive solution for data warehousing, data integration, data analysis, data science, big data, and machine learning.

Data Fundamentals is a crucial aspect of working with data in Azure. It's essential to have a solid understanding of core concepts such as relational and non-relational data, big data, warehousing, real-time analytics, and data visualization.

Relational data is a fundamental concept in data fundamentals, and Azure offers relational database services like Azure SQL Database. Non-relational database fundamentals can be learned using Azure Cosmos DB.

Azure provides a range of tools and services to help manage big data, including Azure ML, which offers multiple services like Azure SQL Database, Azure Cosmos DB, and Azure Data Lake.

Here's a breakdown of the key concepts in data fundamentals:

By understanding these core concepts, you'll be well-equipped to tackle more advanced data-related tasks and projects in Azure.

Monitoring is a crucial aspect of Azure Fundamentals. You can enable Windows Performance Counters in Azure for monitoring.

To do this, you'll need to follow the steps outlined in the Microsoft Azure documentation. Microsoft Azure provides detailed instructions on how to enable Windows Performance Counters in Azure for monitoring.

Monitoring also involves enabling Linux Performance Counters in Azure for monitoring. This allows you to collect performance data from your Linux-based systems.

Enabling Linux Performance Counters in Azure for monitoring is a relatively straightforward process. You can find the necessary instructions in the Microsoft Azure documentation.

In addition to performance counters, you can also enable Windows Event Logs in Azure for monitoring. This allows you to collect and analyze event log data from your Windows-based systems.

Similarly, you can enable Linux System Logs in Azure for monitoring. This helps you collect and analyze system log data from your Linux-based systems.

Azure Monitor Workbooks is another powerful tool for monitoring in Azure. It allows you to create custom workbooks that provide real-time insights into your Azure resources.

Here are some key monitoring tools in Azure:

Developing solutions in Azure is a breeze with the right tools and knowledge. You can get started creating end-to-end solutions in Azure with a course designed for developers. This course will teach you how to create, maintain, and deploy web apps more efficiently using Azure App Service.

Azure App Service is a great place to start, as it allows you to create web apps quickly and easily. With Azure App Service, you can build web apps that are scalable, secure, and reliable. You can also use Azure Functions to build simple APIs and microservices.

Azure also offers a range of services for web, mobile, and IoT development. Services like Azure App Services and Azure IoT Edge are designed to help you build and deploy cloud-based applications quickly and easily. With Azure, you can focus on building your application, while Azure takes care of the infrastructure and maintenance.

Here are some key features of Azure App Service:

By using Azure App Service and other Azure services, you can develop solutions quickly and efficiently. With Azure, you can focus on building your application, while Azure takes care of the infrastructure and maintenance.

Developing a plan is crucial to achieving your goals in developing solutions. Instructor-led training provides a structured learning environment with dedicated support from technical experts.

To make a training plan, you can start by identifying your starting point. For example, if you're a fresher or a global working professional, you can begin with Azure, a powerful cloud management platform that offers flexibility and a wide range of online application options.

Azure is the right platform for you if you want to learn cloud management. You can start building your first app and get an insight into the platform that will help you achieve your goals.

Here are some topics to consider when creating your training plan:

These topics will give you a solid foundation in Azure and help you prepare for certifications. You can also get help to identify the right starting point for your training journey and make an individualized plan to build your skills for the future.

MLOps is a set of practices that helps integrate machine learning activities into the larger framework of developing and delivering software. It merges with DevOps activities of the overall software engineering, driving efficiency in developing software at an enterprise scale.

MLOps is a paradigm shift in the way ML would integrate within the larger software development framework. Traditionally, ML experts would build models and it would be left to software developers and system administrators to integrate these models into the overall application. Using DevOps principles in Machine learning is increasingly enhancing the overall effectiveness of building an overall solution.

Azure Machine Learning has capabilities to integrate with overall DevOps systems like Azure DevOps and GitHub integration. This allows for seamless collaboration and automation of machine learning workflows.

Here are the key activities in MLOps:

These activities are essential for delivering high-quality machine learning models that meet the business requirements.

In Azure Machine Learning, MLOps is achieved through the use of workspaces, compute instances, and pipelines. A workspace is a centralized place to manage resources required for training a model. Compute instances are used to select a compute instance with desired CPU, GPU, RAM, and Storage. Pipelines are used to define a dataflow for training a machine learning model.

By following the MLOps approach, data scientists and developers can work together to build and deploy machine learning models that deliver business value.