Cloud Computing Training in Dresden

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level data engineers who wish to use Azure Data Factory to implement scalable and efficient data integration solutions in the cloud. By the end of this training, participants will be able to:

• Understand the core concepts and architecture of Azure Data Factory (ADF).
• Learn to create, schedule, and monitor data pipelines.
• Integrate with various data sources, perform data transformation, and manage data flows.
• Implement best practices for error handling and performance optimization.
• Secure and monitor data pipelines for production environments.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level IT professionals who wish to gain expertise in managing cloud infrastructure and services on Google Cloud. By the end of this training, participants will be able to:

• Set up and configure cloud environments using Google Cloud services.
• Manage and configure virtual machines, storage, and networking.
• Deploy and monitor applications in GCP environments.
• Implement security best practices and identity management.
• Use Google Cloud tools for automation and troubleshooting.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data scientists and healthcare professionals who wish to leverage AI for advanced healthcare applications using Google Colab. By the end of this training, participants will be able to:

• Implement AI models for healthcare using Google Colab.
• Use AI for predictive modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics. By the end of this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data professionals who wish to apply time series forecasting techniques to real-world data using Google Colab. By the end of this training, participants will be able to:

• Understand the fundamentals of time series analysis.
• Use Google Colab to work with time series data.
• Apply ARIMA models to forecast data trends.
• Utilize Facebook’s Prophet library for flexible forecasting.
• Visualize time series data and forecasting results.

This instructor-led, live training in Dresden (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab. By the end of this training, participants will be able to:

• Understand the core concepts of reinforcement learning algorithms.
• Implement reinforcement learning models using TensorFlow and OpenAI Gym.
• Develop intelligent agents that learn through trial and error.
• Optimize agents' performance using advanced techniques such as Q-learning and deep Q-networks (DQNs).
• Train agents in simulated environments using OpenAI Gym.
• Deploy reinforcement learning models for real-world applications.

This instructor-led, live training in Dresden (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab. By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Dresden (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab. By the end of this training, participants will be able to:

• Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate and manage large-scale machine learning projects in Google Colab.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply NLP techniques using Python in Google Colab. By the end of this training, participants will be able to:

• Understand the core concepts of natural language processing.
• Preprocess and clean text data for NLP tasks.
• Perform sentiment analysis using NLTK and SpaCy libraries.
• Work with text data using Google Colab for scalable and collaborative development.

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level system administrators who wish to learn essential concepts and practical skills required for effective management and optimization of Nutanix environments. By the end of this training, participants will be able to:

• Understand Nutanix Enterprise Cloud Platform and its components.
• Install, configure, and manage Nutanix Clusters.
• Gain proficiency in administering the Acropolis Hypervisor (AHV).
• Perform advanced management tasks and troubleshooting.
• Implement best practices for high availability, disaster recovery, and security.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment. By the end of this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level IT professionals who wish to manage and operate the Google Cloud Platform. By the end of this training, participants will be able to:

• Understand the core services and infrastructure of GCP.
• Learn how to manage networks and virtual machines.
• Understand and apply general security best practices in GCP.
• Explore Kubernetes and container management in GCP.
• Learn about storage options and how to configure them.
• Deploy and configure databases on GCP.

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level data scientists who wish to learn how to create meaningful and visually appealing data visualizations. By the end of this training, participants will be able to:

• Set up and navigate Google Colab for data visualization.
• Create various types of plots using Matplotlib.
• Utilize Seaborn for advanced visualization techniques.
• Customize plots for better presentation and clarity.
• Interpret and present data effectively using visual tools.

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level developers and data analysts who wish to learn Python programming from scratch using Google Colab. By the end of this training, participants will be able to:

• Understand the basics of Python programming language.
• Implement Python code in Google Colab environment.
• Utilize control structures to manage the flow of a Python program.
• Create functions to organize and reuse code effectively.
• Explore and use basic libraries for Python programming.

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab. By the end of this training, participants will be able to:

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data engineers, cloud architects, and business intelligence professionals who wish to master data management in industry-specific cloud environments. By the end of this training, participants will be able to:

• Understand the principles and components of data fabrics.
• Design and implement data integration and orchestration solutions.
• Ensure data quality and governance within industry cloud platforms.
• Utilize data fabrics to support industry-specific data analysis and decision-making.
• Stay current with emerging trends and technologies in cloud data management.

This instructor-led, live training in Dresden (online or onsite) is aimed at beginner-level IT professionals, business analysts, and project managers who wish to understand the fundamentals of cloud computing and its applications in various industries. By the end of this training, participants will be able to:

• Identify the key characteristics and benefits of cloud computing.
• Understand the differences between various cloud service and deployment models.
• Recognize the importance of industry-specific cloud solutions.
• Assess the security and compliance aspects of cloud platforms.
• Explore emerging trends and future directions in cloud technology.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level cloud engineers and operations managers who wish to improve their cloud infrastructure management using Kubiya AI. By the end of this training, participants will be able to:

• Integrate Kubiya AI into cloud infrastructure.
• Automate cloud resource provisioning and management.
• Optimize cloud costs using AI-powered analytics.
• Enhance cloud security with AI-driven monitoring.

This instructor-led, live training in Dresden (online or onsite) is aimed at IT professionals who wish to learn the fundamentals of Alibaba Cloud and how to use its cloud computing services. By the end of this training, participants will be able to:

• Understand the Alibaba Cloud solution architecture.
• Learn about the Alibaba Cloud core products and services.
• Understand the capabilities of Alibaba Cloud in dealing with big data and databases.

This instructor-led, live training in Dresden (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9. By the end of this training, participants will be able to:

• Set up and configure AWS Cloud9 for DevOps workflows.
• Implement continuous integration and continuous delivery (CI/CD) pipelines.
• Automate testing, monitoring, and deployment processes using AWS Cloud9.
• Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilize source control systems like GitHub or GitLab within AWS Cloud9.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment. By the end of this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda. By the end of this training, participants will be able to:

• Understand the fundamentals of serverless architecture.
• Set up AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications using AWS Lambda.
• Integrate AWS Lambda with other AWS services such as API Gateway and S3.
• Optimize serverless applications for performance and cost efficiency.

This instructor-led, live training in Dresden (online or onsite) is aimed at intermediate-level developers, system administrators, and cloud architects who wish to use NoCloud for their cloud solutions. By the end of this training, participants will be able to:

• Set up a development environment that includes NoCloud, Docker, Kubernetes, and Helm.
• Create and manage cloud services with NoCloud.
• Understand the lifecycle of a cloud service in NoCloud.
• Integrate NoCloud with existing infrastructure or virtual resources.
• Troubleshoot common issues in a NoCloud environment.

In this instructor-led, live training in Dresden, participants will learn how to set up and manage a production-scale container environment using Kubernetes on Google Cloud.  By the end of this training, participants will be able to:

• Configure and manage Kubernetes on Google Cloud.
• Deploy, manage and scale a Kubernetes cluster.
• Deploy containerized (Docker) applications on Google Cloud.
• Migrate an existing Kubernetes environment from on-premise to Google Cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery in Kubernetes.

This instructor-led, live training in Dresden (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.By the end of this training, participants will be able to:

• Install and configure Power BI.
• Evaluate the various data solutions offered by cloud providers such as Azure.
• Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
• Apply tools and techniques to clean data in preparation for analysis.
• Build reporting and analytics solutions based on on-premise as well as cloud data.
• Integrate data analytics solutions with a data warehouse.
• Mitigate data security risks and ensure data privacy.

This instructor-led, live training in Dresden (online or onsite) is aimed at developers and DevOps engineers who wish to utilize a serverless approach for building enterprise applications in Kubernetes. By the end of this training, participants will be able to:

• Setup and configure the Kubernetes system to start developing with a serverless architecture.
• Understand the concepts and principles foundational to serverless environments.
• Operate toolchains necessary to serverless development and integrate it with Kubernetes components.
• Practice their skill in Python programming language and apply it to implement  serverless systems.
• Secure enterprise applications that are deployed through a serverless framework on Kubernetes.
• Utilize modern cloud computing methods in optimizing DevOps task processing workflows.

This instructor-led, live training in Dresden (online or onsite) is aimed at cloud engineers and developers who wish to use CloudFormation to manage infrastructure resources within the AWS ecosystem. By the end of this training, participants will be able to:

• Implement CloudFormation templates to automate infrastructure management.
• Integrate existing AWS resources into CloudFormation.
• Use StackSets to manage stacks across multiple accounts and regions.

This instructor-led, live training in Dresden (online or onsite) is aimed at developers who wish to integrate a DynamoDB NoSQL database into a web application hosted on AWS. By the end of this training, participants will be able to:

• Set up the necessary development environment to start integrating data into DynamoDB.
• Integrate DynamoDB into web applications and mobile applications.
• Move data in AWS with AWS services.
• Implement operations with AWS DAX.

This instructor-led, live training in Dresden (onsite or remote) is aimed at engineers who wish to deploy and manage IoT devices on AWS. By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led, live training in Dresden (online or onsite) is aimed at developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices. By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

This instructor-led, live training in Dresden (onsite or remote) is aimed at developers who wish to use AWS Lambda to build and deploy services and applications to the cloud, without needing to worry about provisioning the execution environment (servers, VMs and containers, availability, scalability, storage, etc.). By the end of this training, participants will be able to:

• Configure AWS Lambda to execute a function.
• Understand FaaS (Functions as a Service) and the advantages of serverless development.
• Build, upload and execute AWS Lambda functions.
• Integrate Lambda functions with different event sources.
• Package, deploy, monitor and troubleshoot Lambda based applications.

This instructor-led, live training in Dresden (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.

Audience Software Engineer/Computer Scientists with adequate concepts of algorithms and
familiarity with at least one programming/scripting language but without any experience of Cloud
Computing Duration 3 days (7 hours / day)

Day 1 - provides end-to-end coverage of fundamental cloud computing topics as they relate to both technology and business. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise. Day 2 - explores technology-related topics that relate to cloud computing platforms. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that address cloud service architecture, cloud security threats and technologies, virtualization and data processing. Day 3 - provides a technical insight into foundational cloud computing platforms. Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) environments are explored as compound patterns, comprised of unique and shared building blocks. This module is structured as a guided tour through these architectural layers, describing primary components, highlighting shared components and explaining how building blocks can be assembled and implemented via cloud computing mechanisms and practices Day 4 - builds upon Day 3 to provide a deep dive into elastic, resilient and multitenant technology architectures, as well as specialized solution architectures, such as cloud bursting and cloud balancing. Through the study of architectural mechanisms, industry technologies and design patterns, both core and extended components are described that combine to realize elasticity, resiliency and multitenancy as primary characteristics of cloud platforms. By leveraging these native and enhanced scalability and failover-related feature-sets, specialized solution architectures are described to enable bursting between clouds and on-premise and cloud environments, as well as the balancing of runtime loads across clouds for performance and failover purposes. Day 5 - presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered previously.

This course has been created for people who want to understand how to benefit from cloud computing. It uses Amazon EC2 example, but can be tailored to other providers.

Audience IT architects, mid-level IT managers, IT consultants Format of the course Currently 100% lectures.

This instructor-led, live training in Dresden (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data. By the end of this training, participants will be able to:

• Install and configure Power BI.
• Evaluate the various data solutions offered by cloud providers such as Azure.
• Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
• Apply tools and techniques to clean data in preparation for analysis.
• Build reporting and analytics solutions based on on-premise as well as cloud data.
• Integrate data analytics solutions with a data warehouse.
• Mitigate data security risks and ensure data privacy.

This course is designed to empower non-technical professionals—such as managers, business analysts, QA testers, and operations personnel—with a foundational understanding of cloud computing. Participants will explore the core concepts, benefits, and practical applications of the cloud to make more informed decisions and contribute effectively to cloud migration initiatives. Emphasis will be placed on demystifying cloud jargon, comparing cloud and on-premise infrastructure, understanding service models, and recognizing how cloud impacts various roles within an organization.   By the end of this course, participants will be able to:

• Understand the key definitions and terminology of cloud computing
• Identify different cloud service models (IaaS, PaaS, SaaS)
• Differentiate between cloud deployment models (Public, Private, Hybrid, Multi-Cloud)
• Compare on-premises vs. cloud-based infrastructure
• Understand cloud advantages: scalability, agility, availability, and cost-efficiency
• Recognize common cloud platforms (AWS, Azure, GCP) and their service offerings
• Comprehend cloud responsibilities: shared responsibility model and role-based access
• Grasp the impact of cloud on software testing, deployment, and monitoring
• Participate effectively in cloud adoption and migration projects
• Appreciate cloud security basics, compliance, and governance principles

This instructor-led, live training in Dresden (online or onsite) is aimed at engineers who wish to use AWS CloudFormation to automate the process of managing AWS cloud infrastructure. By the end of this training, participants will be able to:

• Enable AWS services to get started managing infrastructure.
• Understand and apply the principle of "infrastructure as code".
• Improve quality and lower the costs of deploying infrastructure.
• Write AWS CloudFormation Templates using YAML.

This course is created for people who face choices which solution to choose for a specific problem. IT Managers, Solution Architects, Test Managers, System Administrators and Developers can benefit from this course by understanding the benefits and costs of available Cloud/SaaS/Iaas solutions.

• Gain foundational knowledge of Citrix Virtual Apps and Desktops 7 
• How to install, configure, and manage a Citrix Virtual Apps and Desktops 7 site and Cloud connectors
• Identify the considerations between Citrix Virtual Apps and Desktops on-premises and the Citrix Virtual Apps and Desktops Service
• How to deliver app and desktop resources

Build a strong foundation in designing and managing a resilient edge computing infrastructure. Learn about open hybrid cloud infrastructures, managing workloads across diverse clouds, and ensuring flexibility and redundancy. This training provides essential knowledge on creating a scalable and secure infrastructure that supports the dynamic needs of modern applications with edge computing.

This instructor-led, live training in Dresden (online or onsite) is aimed at cloud administrators, cloud architects, technology heads, and financial analysts who wish to record, manage, monitor, and process financial assets of an organization in the cloud. By the end of this training, participants will be able to use FinOps practices in an organization to forecast costs, optimize processes, and perform financial management operations in the cloud.

This instructor-led, live training in Dresden (online or onsite) is aimed at programmers and developers who wish to use Fn in creating serverless applications and services. By the end of this training, participants will be able to:

• Set up Fn to create directories and functions.
• Create applications using different programming languages.
• Monitor functions to resolve issues at the development and deployment stages.

This instructor-led, live training in Dresden (online or onsite) is aimed at system administrators who wish to manage and deploy G Suite for organizations. By the end of this training, participants will be able to:

• Apply Google security practices with 2SV and SSO.
• Monitor domain and services usage through reports in the Admin Console.
• Set up and configure a G Suite Admin Console.

Summary:

• Basics of IoT architecture and functions
• “Things”, “Sensors”, Internet and the mapping between business functions of IoT
• Essential of all IoT software components- hardware, firmware, middleware, cloud and mobile app
• IoT functions- Fleet manager, Data visualization, SaaS based FM and DV, alert/alarm, sensor onboarding, “thing” onboarding, geo-fencing
• Basics of IoT device communication with cloud with MQTT.
• Connecting IoT devices to AWS with MQTT (AWS IoT Core).
• Connecting AWS IoT core with AWS Lambda function for computation and data storage using DynamoDB.
• Connecting Raspberry PI with AWS IoT core and simple data communication.
• Hands on with Raspberry PI and AWS IoT Core to build a smart device.
• Sensor data visualization and communication with web interface.

This instructor-led, live training in Dresden (online or onsite) is aimed at cloud engineers who wish to store objects and unstructured data using MinIO. By the end of this training, participants will be able to:

• Provide an alternative to Unix Commands with the MinIO Client.
• Use MinIO to build high performance infrastructures for machine learning, analytics, and more.
• Deploy MinIO on Kubernetes for orchestrated deployment to scale.

This instructor-led, live training in Dresden (onsite or remote) is aimed at developers who wish to use OpenFaas to create, build, test, debug and deploy event-driven functions without needing to worry about managing the underlying server infrastructure. By the end of this training, participants will be able to:

• Install and configure OpenFaas.
• Package any binary or code as a serverless function without repetitive boiler-plate coding.
• Decouple from AWS Lambda to avoid lock-in.
• Deploy event-driven functions to an on-premise server or to the cloud.

This instructor-led, live training in Dresden (online or onsite) is aimed at software engineers who wish to develop parallel applications using OpenMP. By the end of this training, participants will be able to:

• Understand and use parallel programming with Fortran in OpenMP.
• Calculate fractals in parallel to render multiple pixels and characters.
• Implement vector programming with SIMD extensions for HPC systems.
• Add parallel blocks for specifying shared memory parallelism.

This instructor-led, live training in Dresden (onsite or remote) is aimed at developers who wish to use Serverless Framework on AWS and other cloud platforms to create and deploy microservice applications. By the end of this training, participants will be able to:

• Set up Serverless Framework to work with compute services such as AWS Lambda.
• Reduce the complexity and cost of deploying microservices on different cloud platforms.
• Emit and capture events and execute functions automatically.

Summery:

• Basics of IoT architecture and functions
• “Things”, “Sensors”, Internet and the mapping between business functions of IoT
• Essential of all IoT software components- hardware, firmware, middleware, cloud and mobile app
• IoT functions- Fleet manager, Data visualization, SaaS based FM and DV, alert/alarm, sensor onboarding, “thing” onboarding, geo-fencing
• Basics of IoT device communication with cloud with MQTT.
• Connecting IoT devices to AWS with MQTT (AWS IoT Core).
• Connecting AWS IoT core with AWS Lambda function for computation and data storage.
• Connecting Raspberry PI with AWS IoT core and simple data communication.
• Alerts and events
• Sensor calibration