This instructor-led, live training in 大阪 (online or onsite) is aimed at intermediate-level Java developers, software engineers, and cloud enthusiasts who wish to harness the power of Azure OpenAI to create intelligent applications.
By the end of this training, participants will be able to:
Understand the principles of Generative AI and its applications.
Set up and manage an Azure OpenAI service.
Integrate OpenAI's models into Java applications.
Deploy AI-powered features within web applications.
This instructor-led, live training in 大阪 (online or onsite) is aimed at intermediate-level cloud engineers and IT professionals who wish to use Terraform to automate the deployment, management, and scaling of infrastructure on the Azure cloud platform.
By the end of this training, participants will be able to:
Understand the basics of Terraform and its benefits in infrastructure as code.
Set up and configure Terraform for Azure.
Develop, test, and deploy Terraform configuration files for various Azure services.
Use Terraform for managing and scaling Azure resources.
Implement best practices for Terraform state management and version control.
Integrate Terraform with Azure DevOps for automated deployments.
Create reusable Terraform modules for consistent resource provisioning.
Utilize advanced features of Terraform for complex infrastructure deployments.
This instructor-led, live training in 大阪 (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to build and deploy AI-powered cloud applications using Microsoft Azure.
By the end of this training, participants will be able to:
Develop event-driven and serverless applications using Azure Functions.
Manage Azure storage solutions and virtual machines.
Deploy and scale web applications using Azure App Service and Docker containers.
Integrate AI, machine learning, and natural language processing using Azure AI Services.
Leverage GitHub Copilot to assist in AI-driven cloud application development.
This instructor-led, live training in 大阪 (online or onsite) is aimed at intermediate-level data engineers who wish to become proficient in utilizing Azure Synapse Analytics for a wide range of data processing, analytics, and visualization.
By the end of this training, participants will be able to:
Understand the core concepts, architecture, and components of Azure Synapse Analytics.
Create, configure, and manage scalable data warehouses using Azure Synapse.
Master the techniques for ingesting, transforming, and loading data (ETL) from various sources into Azure Synapse.
Optimize query performance, secure data, and integrate Azure Synapse with Power BI and other tools to visualize data and share insights.
This instructor-led, live training in 大阪 (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions.
By the end of this training, participants will be able to:
Understand the architecture and key features of Azure Data Lake Storage Gen2.
Optimize data storage and access for cost and performance.
Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
Develop solutions using the Azure Data Lake Storage Gen2 API.
Troubleshoot common issues and optimize storage strategies.
This instructor-led, live training in 大阪 (online or onsite) is aimed at security administrators who wish to learn how to configure Azure cloud security to secure workloads running in Azure.
By the end of this training, participants will be able to:
Configure host and network security.
Configure Azure advanced security options.
Use Azure to secure cloud computing workloads.
Use endpoint protection services security against malware and viruses.
Secure container workloads that are running in Azure.
This instructor-led, live training in 大阪 (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.
By the end of this training, participants will be able to:
Write highly-accurate machine learning models using Python, R, or zero-code tools.
Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
Use Azures interactive workspace to collaboratively develop ML models.
Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
This training permits delegates to improve their Microsoft Azure solution design skills.
After this training the delegate will understand the features and capabilities of Azure services, to be able to identify trade-offs, and make decisions for designing public and hybrid cloud solutions.
During training the appropriate infrastructure and platform solutions to meet the required functional, operational, and deployment requirements through the solution life-cycle will be defined.
This instructor-led, live training in 大阪 (online or onsite) is aimed at DevOps engineers, developers, and project managers who wish to utilize Azure DevOps to build and deploy optimized enterprise applications faster than traditional development approaches.
By the end of this training, participants will be able to:
Understand the fundamental DevOps vocabulary and principles.
Install and configure the necessary Azure DevOps tools for software development.
Utilize Azure DevOps tools and services to continuously adapt to the market.
Build enterprise applications and evaluate current development processes upon Azure DevOps solutions.
Manage teams more efficiently and accelerate software deployment time.
Adopt DevOps development practices within the organization.
This instructor-led, live training in 大阪 (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis.
By the end of this training, participants will be able to:
Build machine learning models with zero programming experience.
Create predictive algorithms with Azure Machine Learning.
Deploy production ready machine learning algorithms.
This instructor-led, live training in 大阪 (online or onsite) is aimed at security administrators who wish to secure Azure workloads.
By the end of this training, participants will be able to:
Administrate host security, network security, and more.
Set up storage and database security in Azure.
Implement security monitoring using Azure resources.
Prevent malicious cyber attacks on data and infrastructures.
This instructor-led, live training in 大阪 (online or onsite) is aimed at developers who wish to learn how to build microservices on Microsoft Azure Service Fabric (ASF).
By the end of this training, participants will be able to:
Use ASF as a platform for building and managing microservices.
Understand key microservices programming concepts and models.
Create a cluster in Azure.
Deploy microservices on premises or in the cloud.
Debug and troubleshoot a live microservice application.
The Azure Bot Service combines the power of the Microsoft Bot Framework and Azure functions to enable rapid development of intelligent bots.
In this instructor-led, live training, participants will learn how to easily create an intelligent bot using Microsoft Azure
By the end of this training, participants will be able to:
Learn the fundamentals of intelligent bots
Learn how to create intelligent bots using cloud applications
Understand how to use the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
Understand how to design bots using bot patterns
Develop their first intelligent bot using Microsoft Azure
Audience
Developers
Hobbyists
Engineers
IT Professionals
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training in 大阪 (onsite or remote) participants will learn the fundamental concepts, components, and services of Microsoft Azure as they step through the creation of a sample cloud application.
By the end of this training, participants will be able to:
Understand the basics of Microsoft Azure
Understand the different Azure tools and services
Learn how to use Azure for building cloud applications
Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Azure is a comprehensive set of cloud services which offers an IoT Suite consisting of preconfigured solutions that help developers accelerate development of IoT projects.
In this instructor-led, live training, participants will learn how to develop IoT applications using Azure.
By the end of this training, participants will be able to:
Understand the fundamentals of IoT architecture
Install and configure Azure IoT Suite
Learn the benefits of using Azure in programming IoT systems
Implement various Azure IoT services (IoT Hub, Functions, Stream Analytics, Power BI, Cosmos DB, DocumentDB, IoT Device Management)
Build, test, deploy, and troubleshoot an IoT system using Azure
Audience
Developers
Engineers
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 大阪 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on Azure.
Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Leverage other AWS managed services to extend an ML application.
This instructor-led, live training in (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.
By the end of this training, participants will be able to:
Build reproducible workflows and machine learning models.
Manage the machine learning lifecycle.
Track and report model version history, assets, and more.
Deploy production ready machine learning models anywhere.
