If you are a developer and you started to feel outside of the Machine Learning world, this session is for you. In this session, we will review the basics of machine learning, how to use existing models and services in standard developer apps, and how to get started with creating your own simple models. Important: we don’t need any specific math or data skills to create ML models. We will cover the basis of Machine Learning.Net, a complete ML framework to work with C#, F# or any other .Net Core language. And I’m a developer first, so do not expect much slides. Instead, we will code a fully functional app from scratch.
Basic Introduction to Machine Learning
OData is a well-established standard that has been used in many software systems for years. Azure makes heavy use of OData and the protocol is supported in client apps like Power BI, too. In this session, MVP and Regional Director Rainer Stropek speaks about the current state of OData. He starts with a quick introduction for those viewers who are not familiar with OData. Next, he shows how OData’s integration with .NET’s LINQ, ASP.NET Core and Entity Framework makes it pretty simple to get you own OData services up and running. Rainer will also critically evaluate OData by talking about areas where Microsoft’s implementation has to improve as well as pros and cons compared to classical REST APIs, gRPC, GraphQL, and JSON:API. If you build data-driven APIs for the web and the cloud, this session is for you. Rainer will use only a few slides. He will focus on code walkthrough and live coding.
Plan Caching & Recompilation in SQL Server
Introduction to Apache Kafka Architecture
Indexing in Azure Data Warehouse
In today’s world, big data processing is a critical task for every organization. Organization spent good amount of time and resources to build data transformation pipeline. Recently Microsoft launched ADF Mapping Data Flow. Mapping Data Flows allow users to quickly transform data at scale. Build resilient data pipelines in an accessible visual environment with our browser-based designer and let ADF handle the complexities of Spark execution.
In the not too distant past Azure SQL Database was known to many as just “cloud-based SQL Server” without the full features needed to encourage a move to it from on-premises SQL Server. Times have changed! Azure SQL Database, with the addition of deployment models like Managed Instance and storage technologies like Hyperscale, has matured into a formidable competitor in the PaaS database space. Join me as I walk you through how these technologies work behind the scenes, what options that gives you as you and your company consider migration, and how the Azure SQL Database family has given companies a powerful platform to store and use their data without much of the management overhead and hassle.