Develop dynamic reports with Microsoft Power BI
Transform and load data, define semantic model relationships and calculations, create interactive visuals, and distribute reports using Power BI.
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Explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.
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In this learning path, the student is exposed to various ways to:
- Source streaming data sources into Microsoft Fabric.
- Use real time Eventstream in Microsoft Fabric.
- Query data in a KQL database in Microsoft Fabric.
- Create real time dashboards in Microsoft Fabric.
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Explore the data warehousing process and learn how to load, monitor, secure, and query a warehouse in Microsoft Fabric.
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Generative Artificial Intelligence (AI) engineering with Azure Databricks uses the platform’s capabilities to explore, fine-tune, evaluate, and integrate advanced language models. By using Apache Spark’s scalability and Azure Databricks’ collaborative environment, you can design complex AI systems.
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Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.
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Azure Database for PostgreSQL is a Platform as a Service database service in the Microsoft cloud. It bases itself on the PostgreSQL open-source relational database and includes built-in high availability, automatic backup and restore, as well as comprehensive security features. The pay-as-you-go pricing model provides predictable performance and near-instant scaling.
In this learning path, you learn the main features of PostgreSQL and how they work in Azure Database for PostgreSQL. You learn about the different Azure Database for PostgreSQL implementation options, and how to configure a server for your needs.
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This learning path prepares you for the task of developing data-driven applications by using Microsoft Azure SQL Database.
You’ll learn how to create and configure an Azure SQL Database, build and deploy database projects using GitHub Actions and Azure Pipelines, and automate the publishing process. Additionally, you’ll explore how to use Data API builder for Azure SQL Database and develop a data API with Azure Web Apps and Static Web App.
Furthermore, you’ll gain skills in importing data via an external REST endpoint, exporting data using an Azure Function, and securing an Azure SQL Database. These essential skills will empower you to effectively develop and manage applications using Azure SQL Database.
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Implement a Machine Learning solution with Azure Databricks
Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
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Implement a Data Analytics Solution with Azure Databricks
Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data analytics workloads in a data lakehouse.
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Validate your technical skills and open doors to new possibilities of advancement with Microsoft Applied Skills.
To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
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Explore DevOps practices using GitHub. Your development and operations teams will experience improved collaboration, agility, continuous integration, continuous delivery, automation, and operational excellence throughout all phases of the application lifecycle.
The opportunity is yours to lead the era of AI with Microsoft. Power your organization’s AI transformation with Microsoft Cloud. The AI you can trust.
