Unsere Microsoft zertifizierten Trainer nehmen dich mit in die Microsoft Welt der KI und ML. Von Azure-Grundlagen bis hin zu KI-Lösungen und Datenplattformen - mit unseren 11 Kursen bist du bereit für jede wichtige Zertifizierung auf deinem Weg zum zertifizierten Azure Data Scientist Associate, Azure Data Engineer Associate, Azure AI Engineer Associate oder Azure Solutions Architect Expert.

Microsoft Azure Fundamentals: 1 or 2 day training

prepares for Microsoft AZ-900 Certificate : Microsoft Azure Fundamentals

Students for this course gain foundational knowledge of cloud services and how those services are provided with Microsoft Azure.

This course is an opportunity to gain knowledge about cloud concepts, core Azure services, Azure pricing, SLA, and lifecycle, and the fundamentals of cloud security, privacy, compliance, and trust.

This course is intended for people beginning to work with cloud-based solutions and services. Students learn a fundamental knowledge of cloud concepts, as well as Azure services, workloads, security, privacy, pricing, and support. In addition, students get familiar with concepts of networking, storage, compute, application support, and application development.

• Cloud concepts
• Core Azure services
• Security, privacy, compliance, and trust
• Pricing and support
• For: Developers, general audience

Microsoft Azure AI Fundamentals : 1 day

prepares for Microsoft AI-900 Certificate : Azure AI Fundamentals

Students for this course gain foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts, both related to Microsoft Azure services.

This course is an opportunity to gain knowledge of common ML and AI workloads and how to implement them on Azure.

• Introduction to AI
• Machine Learning
• Computer Vision
• Natural Language Processing
• Conversational AI
• For AI Engineers, ML Engineers, Data Scientists, general audience

Designing and Implementing a Data Science solution on Azure : 3 days

prepares for Microsoft DP-100 Certificate : Azure Data Scientist Associate

Students gain knowledge of data science and machine learning and how to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

• Introduction to Azure Machine Learning
• “No-code” Machine Learning with Designer
• Experiments and training models
• Work with data
• Compute Contexts
• Orchestrating Operations with Pipelines
• Deploying and Consuming Models
• Training Optimal Models
• Interpreting Models
• Monitoring Models
• For everyone with prerequisites: Programming skills, Azure Fundamentals knowledge

Designing and Implementing a Microsoft Azure AI solution : 4 days

prepares for Microsoft AI-102 Certificate : Azure AI Engineer Associate

Students for this course gain subject matter expertise using cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.

Students learn how to fulfil the responsibilities for an Azure AI Engineer include analyzing requirements for AI solutions, recommending the appropriate tools and technologies, and designing and implementing AI solutions that meet scalability and performance requirements.

Students learn how to translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and software developers to build complete end-to-end solutions.

• Introducing Azure Cognitive Services
• Creating Bots
• Enhancing Bots with QnA Maker
• Learn how to Create Language Understanding with LUIS
• Enhance your Bot with LUIS
• Integrate Cognitive Service with Bots and Agents
• For everyone with prerequisites: Programming skills, Azure Fundamentals knowledge

Microsoft Azure Architect Technologies : 5 days

prepares for Microsoft AZ-303 Exam : Microsoft Azure Architect Technologies

Students for this course gain subject matter expertise in designing and implementing solutions on Microsoft Azure. Aspects include concepts like compute, networking, data storage, and security.

Students learn how to fulfil the responsibilities for an Azure Solutions Architect including advising stakeholders, translate business requirements into technical cloud solutions, and designing secure, scalable, and reliable cloud solutions.

Students also learn about networking, virtualization, identity, security, business continuity, disaster recovery, data platform, budgeting, and governance. In addition, students learn how these aspects can impact the solution. Lastly, students learn how to use their Azure administration skills, as well as Azure development and DevOps processes knowledge.

• Implementing and monitoring of Azure infrastructure
• Security and solution management
• Design and implement solutions for apps
• Creating and deploying apps on various resources
• Implement and manage data platforms
• For everyone with prerequisites: Azure developers, experienced with application development on Azure, seeking to become a solutions architect

Microsoft Azure Architect Design : 4 days

prepares for Microsoft AZ-304 Exam : Microsoft Azure Architect Design

Students for this course gain subject matter expertise in designing and implementing solutions on Microsoft Azure. Aspects include concepts like compute, networking, data storage, and security.

Students learn how to fulfil the responsibilities for an Azure Solutions Architect including advising stakeholders, translate business requirements into technical cloud solutions, and designing secure, scalable, and reliable cloud solutions.

Students also learn about networking, virtualization, identity, security, business continuity, disaster recovery, data platform, budgeting, and governance. In addition, students learn how these aspects can impact the solution. Lastly, students learn how to use their Azure administration skills, as well as Azure development and DevOps processes knowledge.

• Implementing and monitoring in Azure
• Security and identity management
• Deploying infrastructure in Azure
• Implement and manage data platforms
• Design business continuity
• Design migration, integration, and disaster recovery
• For everyone with prerequisites: Azure developers, experienced with application development on Azure, seeking to become a solutions architect

Designing Microsoft Azure Infrastructure Solutions : 4 days

prepares for Microsoft AZ-305 Exam : Design Microsoft Azure Infrastructure Solutions

Students for this course gain subject matter expertise in designing and implementing solutions on Microsoft Azure. Aspects include concepts like compute, networking, data storage, data integration, app architectures, monitoring, security, networking, and migration solutions.

Students learn how to fulfil the responsibilities for an Azure Solutions Architect including advising stakeholders, translate business requirements into technical cloud solutions, and designing secure, scalable, and reliable cloud solutions.

Students also learn about virtualization, identity and access management, security, business continuity, disaster recovery, data platform, budgeting, and governance. In addition, students learn how these aspects can impact the solution. Lastly, students learn how to use their Azure administration skills, as well as Azure development and DevOps processes knowledge.

• Design a governance solution in Azure
• Work with management groups, subscriptions, resource groups and resources
• Work with policies, tags, and blueprints
• Design the most common Azure solutions including VMs, Batch, Functions, Logic Apps, Container Instances, App Services and Kubernetes Service
• Design data storage and data integration solutions
• Work with storage solutions, from files to databases, and streaming event
• Work with data integration solutions including Data Factory, Data Lake, Azure Databricks, and Azure Synapse Analytics
• Design app architectures
• Design event-driven architectures with Azure services
• Design identity and access management solutions
• Design monitoring solutions including Azure Monitor, Log Analytics, and more
• Design network architectures in Azure
• Design business continuity solution with backup and recovery services and scenarios
• Design migration solutions
• For everyone with prerequisites: Azure developers or administrators, experienced with application development on Azure, seeking to become a solutions architect

Data Engineering on Microsoft Azure: 4 days

prepares for the Microsoft DP-203 Exam : Azure Data Engineer Associate

In this course, the students will implement various data platform technologies into solutions that are in line with business and technical requirements including on-premises, cloud, and hybrid data scenarios incorporating both relational, No-SQL data, and data warehouse data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data.

The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions.

• Introduction Azure for the Data Engineer
• Introduction to Azure Synapse Analytics and Azure Databricks
• Introduction to Data Lake architecture
• Working with Data Storage
• Working with compute options
• Run interactive queries with serverless SQL pools in Azure Synapse
• Secure data and manage users in Azure Synapse
• Working with data in Azure Databricks
• Extracting, transforming, and loading data into a Data Warehouse with Apache Spark
• Ingesting and loading data into the Synapse Data Warehouse
• Transforming data with Azure Data Factory
• Integrating notebooks with Azure Data Factory or Azure Synapse Analytics
• End-to-end security with Azure Synapse Analytics
• Hybrid Transactional Analytical Processing (HTAP) between Cosmos DB and Azure Synapse Analytics
• Processing real-time streaming data with Stream Analytics
• Stream processing solutions with Event Hubs and Azure Databricks

Migrate NoSQL workloads to Azure Cosmos DB : 1 day

complements the Azure Data Scientist Associate role

Students get an introduction into Cosmos DB and its APIs. Students will gain knowledge on migration considerations and best practices. Next, students will learn how to migrate MongoDB workloads to Cosmos DB, including planning, data migration and application migration. The same holds for migrating Cassandra workloads to Cosmos DB.

• Introduction to Cosmos DB
• Introduction to Cosmos DB migrations
• Migrate MongoDB workloads to Cosmos DB
• Migrate Cassandra workloads to Cosmos DB
• For everyone with prerequisites: Data engineering skills and fundamental Azure knowledge

Migrate Open Source Data Workloads to Azure : 1 day

complements the Azure Data Scientist Associate role

Students get an introduction into Azure database services for MySQL and PostgreSQL. Students will gain knowledge on migration considerations and best practices. Next, students will learn how to migrate MySQL workloads to Azure. Next topic is migrating PostgreSQL workloads to Azure. Next, students learn how to protect the data in Azure, how to setup monitoring of Azure services and how to tune the database services. Finally, students learn how to migrate Redis Cache workloads to Azure.

• Introduction to open source database migrations to Azure
• Migrate MySQL workloads to Azure
• Migrate PostgreSQL workloads to Azure
• Protecting data
• Monitoring database services
• Tuning databases
• Migrate Redis Cache workloads to Azure
• For everyone with prerequisites: Data engineering skills and fundamental Azure knowledge

Implementing a Machine Learning Solution with Microsoft Azure Databricks : 1 day

complements the Azure Data Scientist Associate role

Students for this course will learn how to use Azure Databricks for machine learning workloads in the cloud. Students will work through the material and hands-on exercises in this course, and will build on their existing data science and machine learning knowledge and learn how to leverage cloud services to perform machine learning at scale.

• Create an Azure Databricks workspace, and manage compute, data, and coding environments for machine learning workloads
• Prepare data and train a machine learning model using Spark ML
• Track model details and register models with MLflow
• Run Azure Machine Learning experiments on Azure Databricks and deploy trained models onto Azure Kubernetes Service and Azure Container Instances using Azure Machine Learning
• For everyone with prerequisites: Data engineers, data scientists, and machine learning engineers, who know the concepts of machine learning and would like to use the scalability of the cloud and bring machine learning models into production.