Master Microsoft Azure and elevate your career with our comprehensive, hands-on course!
What you'll learn
Create and manage Azure Machine Learning workspaces.
Develop and submit machine learning pipelines using Azure ML Designer and SDK.
Understand AI concepts and explore common AI workloads.
Utilize Azure Cognitive Services for computer vision and natural language processing (NLP).
Learn data representation and storage options in Azure, including SQL and Cosmos DB.
Set up and manage Azure SQL databases, ensuring security and scalability.
Understand the fundamentals of cloud computing and Azure's core services.
Create and manage virtual machines, storage solutions, and virtual networks in Azure.
Implement high availability and scaling solutions for Azure resources.
Create and manage Azure Data Lake, including data processing and analytics.

Requirements
Basic Understanding of Cloud Concepts: Familiarity with cloud computing principles.
Fundamental Knowledge of Machine Learning: Basic understanding of machine learning concepts and terminology.
Programming Experience: Proficiency in at least one programming language, preferably Python.
Azure Basics: Prior experience or knowledge of Microsoft Azure's core services.

Description
Welcome to the Microsoft Azure Mastery Course, a comprehensive learning journey designed to equip you with the knowledge and skills needed to excel in the rapidly evolving field of cloud computing and data science. This course covers a wide array of topics, from foundational principles to advanced applications, ensuring a well-rounded understanding of Microsoft Azure's extensive capabilities.Microsoft Azure is one of the leading cloud platforms, offering a vast range of services that support various business needs, including data storage, machine learning, AI, and database management. With the increasing demand for cloud-based solutions, proficiency in Azure has become a valuable asset for IT professionals, data scientists, and developers.Our course is structured into distinct sections, each focusing on a specific aspect of Azure. You will start with an introduction to Azure's data science capabilities and progress through foundational AI concepts, data fundamentals, and relational database management. Practical sections on cognitive services and AI-based chatbot creation will enhance your hands-on experience, while sections on Azure essentials and data lake management will solidify your foundational knowledge.Whether you are preparing for Microsoft certification exams or seeking to expand your practical skills, this course offers a blend of theoretical knowledge and practical application. You will engage with interactive lessons, real-world scenarios, and step-by-step guides, ensuring a robust learning experience.By the end of this course, you will be well-prepared to leverage Microsoft Azure's powerful tools and services, ready to tackle complex challenges and drive innovation in your organization. Welcome to your journey towards Azure mastery!Section 1: DP-100 Microsoft Azure Data Science ExamIn this section, you will gain comprehensive knowledge and hands-on experience with Microsoft Azure's data science capabilities. Starting with an introduction to the course and exam requirements, you will learn how to create and manage an Azure Machine Learning workspace, including its settings through the portal and Azure ML Studio. The section covers the creation and management of datasets, compute instances, and clusters, and takes you through building and submitting machine learning pipelines. You'll delve into custom code, error handling, and understanding complex pipelines, ensuring a thorough grasp of Azure ML Designer and SDK setup. The section also introduces AutoML, model registration, deployment strategies, and production compute targets, preparing you for real-world applications of Azure's machine learning tools.Section 2: AI-900 MS Azure AI FundamentalsThis section is designed to provide a foundational understanding of artificial intelligence (AI) and its applications within Microsoft Azure. Beginning with an introduction to AI and the AI-900 exam requirements, you will explore common AI workloads, responsible AI principles, and privacy and security considerations. The section covers various machine learning types, dataset features, and the training and validation process. You'll gain practical experience with AutoML, machine learning designer tools, and common computer vision and natural language processing (NLP) workloads, using Azure's services to build and deploy models without extensive coding. This section is perfect for those new to AI and seeking to understand its fundamentals and applications.Section 3: DP-900 Azure Data FundamentalsSection 3 focuses on the basics of data representation, storage options, and data workloads within Azure. You'll start with an introduction to the DP-900 exam requirements, followed by an exploration of relational and non-relational databases, including normalization, SQL, and NoSQL databases. Practical demos will guide you through creating databases, views, stored procedures, and indexes, as well as deploying and managing Azure SQL and Cosmos DB. This section also covers modern data warehousing, Azure Data Factory, and data visualization tools like Power BI, providing a comprehensive overview of Azure's data services and their practical applications.Section 4: DP-300 Azure Relational DBAThis section prepares you for managing relational databases in Azure. After an introduction and overview of the DP-300 exam requirements, you'll learn about Azure's SQL database options, creating and managing Azure VMs, and deploying SQL servers. Topics include functional and scaling requirements, availability and backup strategies, and security measures for SQL databases. You will explore scaling techniques, SQL database replicas, sharding, and data synchronization. The section also covers migration and upgrade strategies, ensuring you are well-equipped to handle Azure SQL database management and optimization.Section 5: Microsoft Azure - BasicsIn Section 5, you will explore the fundamentals of Microsoft Azure and cloud computing. Starting with an overview of cloud computing principles and the history of Azure, you'll learn about key cloud services, deployment models, and the architecture of Microsoft Azure. The section covers account registration, the Azure portal, and various tools for managing Azure services, including PowerShell and CLI. You will gain practical knowledge of Azure virtual machines, storage options, and networking configurations, preparing you to leverage Azure's capabilities for various cloud-based applications and services.Section 6: Azure Practical - Cognitive ServicesSection 6 provides an in-depth look at Azure's cognitive services, focusing on practical applications. You'll begin with an introduction to Azure Cognitive Services and the creation of a free tier account. The section covers vision APIs, computer vision, face API, custom vision, form recognizer, and video indexer, with hands-on exercises for each service. You'll also explore speech APIs, including speech-to-text, text-to-speech, and translation services. Additionally, the section covers language services, sentiment analysis, entity recognition, and the QnA Maker, providing a comprehensive understanding of how to implement cognitive services in real-world scenarios.Section 7: Azure Cognitive Services - Creating an AI-Based ChatbotIn this section, you will learn how to create an AI-based chatbot using Azure Cognitive Services. Starting with an introduction to the course, you'll explore the QnA service, Java application creation, and resolving issues within the application. The section provides practical steps for running and deploying the chatbot application, ensuring you have the skills to develop and manage AI-based chatbots effectively.Section 8: Microsoft Azure - EssentialsSection 8 covers the essential principles and services of Microsoft Azure. Beginning with an introduction to Azure essentials, you will learn about the five principles of cloud computing, the history of cloud computing, and key types of cloud services. The section also covers cloud deployment models, Azure's architecture, and account registration. Practical demos will guide you through the Azure Resource Manager (ARM) and Azure Service Manager (ASM) portals, subscription management, and the installation and usage of Azure PowerShell and CLI. This section provides a solid foundation for understanding and utilizing Microsoft Azure's core services.Section 9: Microsoft Azure - Data LakeThe final section focuses on Azure Data Lake, offering an introduction and overview of its components and services. You will learn how to create analytics accounts, process data within the Data Lake Store, and use the USQL language for data manipulation. The section covers defining analytics units, job stages, data ingestion, and processing multiple files. You'll also explore Azure Data Lake's integration with Visual Studio, monitoring and analyzing jobs, and using PowerShell and CLI for data management. The section concludes with a summary of Azure Data Lake's pricing and capabilities, equipping you with the knowledge to leverage this powerful data storage and processing solution.ConclusionBy the end of this comprehensive course, you will have a deep understanding of Microsoft Azure's data science, AI, data fundamentals, relational database management, cognitive services, and cloud computing basics. Each section provides practical knowledge and hands-on experience, preparing you for various Azure certification exams and real-world applications of Azure's services. Whether you are a beginner or an experienced professional, this course will enhance your skills and help you leverage Azure's capabilities to their fullest potential.

Overview
Section 1: DP-100 Microsoft Azure DS Exam
Lecture 1 Introduction to Course
Lecture 2 Exam Requirements
Lecture 3 Create an Azure Machine Learning Workspace
Lecture 4 Azure ML Workspace Settings - Portal
Lecture 5 Azure ML Studio Settings
Lecture 6 Data Stores and Datasets
Lecture 7 Create Additional Datasets
Lecture 8 Create an Experiment Compute Instance
Lecture 9 Manage Multiple Compute Instances
Lecture 10 Create Compute Targets and Clusters
Lecture 11 Creating our First ML Pipeline
Lecture 12 Submitting Pipeline
Lecture 13 Custom Code in Pipeline
Lecture 14 Understanding Complicated Pipeline
Lecture 15 Evaluating Execution Results
Lecture 16 Errors in Azure ML Designer
Lecture 17 Various Modules of Azure ML Designer
Lecture 18 Setup SDK
Lecture 19 Create ML Workspace using SDK
Lecture 20 Simple Program in Python
Lecture 21 Train Model using SDK
Lecture 22 Submit Experiment using SDK
Lecture 23 Create a Pipeline by using SDK
Lecture 24 AutoML Overview
Lecture 25 AutoML with SDK
Lecture 26 Understanding what is Hyper drive
Lecture 27 Register a Trained Model
Lecture 28 Create Production Compute Targets
Lecture 29 Deploy AutoML
Lecture 30 Create an AutoML Endpoint
Lecture 31 Deploy ML Designer for Real Time
Lecture 32 Deploy SDK Models
Lecture 33 Publish a Pipeline for Batch Inference
Lecture 34 Conclusion
Section 2: AI-900 MS Azure AI Fundamentals
Lecture 35 Introduction to Course
Lecture 36 What is Artificial Intelligence
Lecture 37 Machine Learning Model
Lecture 38 AI-900 Exam Requirements
Lecture 39 Common AI workloads
Lecture 40 Guiding principles for Responsible AI
Lecture 41 Privacy and Security
Lecture 42 Transparence and Accountability
Lecture 43 MS Official website for AI Principles
Lecture 44 Common ML Types
Lecture 45 Dataset Features and Labels
Lecture 46 Training and Validation Dataset
Lecture 47 FPR and AUC
Lecture 48 Auto ML
Lecture 49 Demo-Create Azure ML Workspace
Lecture 50 Use AutoML to Build a no-Code Model
Lecture 51 ML Designer
Lecture 52 ML Designer to Build a no-Code Model
Lecture 53 Common types of Computer Vision Workloads
Lecture 54 Computer Vision Services in Microsoft Azure
Lecture 55 Using Computer Vision Service
Lecture 56 Using Custom Vision Service
Lecture 57 NLP Workloads
Lecture 58 NLP Services
Lecture 59 Common Types of Conversational AI workloads
Lecture 60 Conversational AI Services in Azure
Lecture 61 Conclusion
Section 3: DP-900 Azure Data Fundamentals
Lecture 62 Introduction to Course
Lecture 63 Exam Requirements
Lecture 64 Describe Ways to Represent Data
Lecture 65 Identify Options For Data Storage
Lecture 66 Describe Common Data Workloads
Lecture 67 Responsibilities For Data Workloads
Lecture 68 Identify Features of Relational Data
Lecture 69 Describe Normalization
Lecture 70 Three Normal Forms
Lecture 71 Demo - Sample Database
Lecture 72 Identify Common Structured Query Language
Lecture 73 Demo - Create A Database View
Lecture 74 Demo - Create A Stored Procedure
Lecture 75 Demo - Create An Index
Lecture 76 Relational DBs Introduction
Lecture 77 Azure Relational DB Options
Lecture 78 Creating Azure SQL Database
Lecture 79 Arm Templates to Manage SQL Databases
Lecture 80 SQL Database Security
Lecture 81 Relational Query Tools
Lecture 82 Introduction to Non Relational DBs
Lecture 83 Non Relational Data Types
Lecture 84 Choose A NoSQL Database
Lecture 85 Azure Non Relational DB Options
Lecture 86 Creation Cosmos DB
Lecture 87 Query Cosmos DB
Lecture 88 Use Arm Templates to Manage Cosmos DB
Lecture 89 Cosmos DB Security
Lecture 90 Cosmos DB Geo-Replication
Lecture 91 Modern Data Warehouse
Lecture 92 Azure Data Factory
Lecture 93 Data Visualization
Lecture 94 Power Bi Content Workflow
Lecture 95 Conclusion
Section 4: DP-300 Azure Relational DBA
Lecture 96 Introduction to Course
Lecture 97 Exam Requirements
Lecture 98 Azure Free Account
Lecture 99 Azure Relational Databases Covered in Exam
Lecture 100 SQL Database Options Overview
Lecture 101 Creating Azure VM and Installing SQL Server
Lecture 102 Connect Database Through Port 1433
Lecture 103 Deploying SQL Server from the Azure Marketplace
Lecture 104 Maintenance Window
Lecture 105 Install Azure SQL Database
Lecture 106 Using Sample Database
Lecture 107 How to Choose an Azure Relational Database
Lecture 108 Evaluate Functional Requirements
Lecture 109 Evaluate Scaling Requirements
Lecture 110 Availability and Backup Requirements
Lecture 111 Security Requirements
Lecture 112 Scaling Azure SQL Database
Lecture 113 Using Elastic Pool Database to Scale
Lecture 114 Scaling SQL VM
Lecture 115 Using SQL DB Replicas and Sharding
Lecture 116 SQL Data Sync
Lecture 117 Prepare a Migration Strategy
Lecture 118 Prepare an Upgrade Strategy
Lecture 119 Run a Migration Assessment
Lecture 120 Perform a Database Migration
Section 5: Microsoft Azure - Basics
Lecture 121 What is Cloud Computing
Lecture 122 Basic of azure cloud
Lecture 123 Basic of azure cloud (Continues)
Lecture 124 Disadvatge of azure cloud
Lecture 125 Disadvatge of azure cloud (Continues)
Lecture 126 Azure cloud component and use
Lecture 127 Azure portal and price of services
Lecture 128 App services
Lecture 129 Mobile services
Lecture 130 Full calculator
Lecture 131 Web application service
Lecture 132 Web application service (Continues)
Lecture 133 Azure virtual network
Lecture 134 DNS Servers and VPN Connectivity
Lecture 135 IBM Cloud Network
Lecture 136 Azure virtual machine
Lecture 137 How to create virtual machine
Lecture 138 Other things in virtual machine
Lecture 139 How to access virtual machine
Lecture 140 Azure virtual storage
Lecture 141 Azure Virtual Storage (Continues)
Lecture 142 Configuring vm networking in azure
Lecture 143 Configuring vm networking in azure (Continues)
Lecture 144 What is vm networking
Lecture 145 Perform configuration management
Lecture 146 Creating a network in cloud
Lecture 147 Subnetting
Lecture 148 Subnetting (Continues)
Lecture 149 Increase and decrease in the number of machines
Lecture 150 How to create sharepoint machine in azure with image
Lecture 151 How to create sharepoint machine in azure with image (Continues)
Lecture 152 How to create windows vm and linux vm
Lecture 153 What is end point
Lecture 154 Public port and private port
Lecture 155 How to create windows and linux machine using portal
Lecture 156 How to make autoscaling of machine
Lecture 157 Adding weband web job applcation to web site
Lecture 158 How to implemnent web site
Lecture 159 How to implemnent web site (Continues)
Lecture 160 Adding machine to availibilty set
Lecture 161 Brief introduction to virtual machines
Lecture 162 What is the scalibility
Lecture 163 Creating a web application
Lecture 164 Database configuration
Lecture 165 Connecting azure to on site network
Lecture 166 Cloud services
Lecture 167 Scaling facilities
Lecture 168 What is a networking
Lecture 169 What is a virtual network
Lecture 170 Virtual area network address
Lecture 171 What is a Webjob
Lecture 172 Creating a virtual machine
Lecture 173 Monitoring
Lecture 174 Server manager - dashboard
Lecture 175 Components
Lecture 176 Local area network
Lecture 177 Reports
Lecture 178 SQL Database
Lecture 179 Recovery Services
Lecture 180 Remote desktop service azure provsion
Lecture 181 Desktop services
Lecture 182 Storsimple
Lecture 183 Cdn,remote app,media service
Lecture 184 How to migarte on premise to azure cloud
Lecture 185 How to migarte on premise to azure cloud (Continues)
Lecture 186 How to purchase additional subscrption
Lecture 187 Billing of azure
Lecture 188 Planing how to migarte on premise work load to azure cloud
Lecture 189 Planing how to Migarte on premise work load to azure cloud (Continues)
Lecture 190 Azure Hdmi
Lecture 191 Azure Machine Learning
Lecture 192 Biztalk Service
Lecture 193 Traffic Manager
Lecture 194 Select a Runbook
Lecture 195 HD Insight
Section 6: Azure Practical - Cognitive Services
Lecture 196 Introduction to Azure Cognitive
Lecture 197 Azure Free Tier Account Creation
Lecture 198 Vision API and Computer Vision Overview
Lecture 199 Computer Vision Hands on
Lecture 200 Computer Vision Hands on Continued
Lecture 201 Face API
Lecture 202 Face API Handson
Lecture 203 Custom Vision
Lecture 204 Custom Vision Handson
Lecture 205 Form and Ink Recognizer Overview
Lecture 206 Video Indexer Handson
Lecture 207 Form Recognizer Hands on
Lecture 208 Cognitive Speech Api
Lecture 209 Speech to Text Service
Lecture 210 Speech to Text Service Handson
Lecture 211 Text to Speech Service
Lecture 212 Text to Speech Service Handson
Lecture 213 Speech Translation Service Overview
Lecture 214 Speech Translation Service Handson
Lecture 215 Speech Recognise Overview
Lecture 216 Speech Recognise REST Api Usage
Lecture 217 Language Service
Lecture 218 Sentiment Analysis and Handson
Lecture 219 Language Translator Service and Handson
Lecture 220 Understanding Service
Lecture 221 Understanding Service Handson Part 1
Lecture 222 Understanding Service Handson Part 2
Lecture 223 Understanding Service Handson Part 3
Lecture 224 Understand Services in Action
Lecture 225 Entity Recognition
Lecture 226 Entity Recognition Handson
Lecture 227 Entity Recognition in Action
Lecture 228 QnA Maker Overview
Lecture 229 QnA Maker Handson
Lecture 230 QnA Maker in Action
Lecture 231 Cognitive Decision Service
Lecture 232 Content Moderator Overview
Lecture 233 Image Moderator Application
Lecture 234 Image Moderator Application Continued
Lecture 235 Text Moderation
Lecture 236 Moderation Application in Action
Lecture 237 Anomaly Detection Overview
Lecture 238 Anomaly Detection Handson Dependecies Import
Lecture 239 Anomaly Azure Conection
Lecture 240 Graph Ploting
Lecture 241 Anomaly Detection Function
Lecture 242 Anomaly Application in Action
Lecture 243 Decision Personalizer Overview
Lecture 244 Decision Personalizer Application Dependency
Lecture 245 Decision Personalizer Application
Lecture 246 Personalizer Service in Action
Lecture 247 Cognitive Web Search Overview
Lecture 248 Cognitive Web Search Subparts
Lecture 249 Cognitive Websearch Application Creation
Lecture 250 Cognitive Websearch Application Creation Continued
Lecture 251 Cognitive Websearch Application in Action
Section 7: Azure Cognitive Services - Creating an AI Based Chatbot
Lecture 252 Introduction to Course
Lecture 253 QnA Service
Lecture 254 Java Application Creation
Lecture 255 Resolving Issues from Application
Lecture 256 Running Java Aaplication
Lecture 257 Service Cleanup
Section 8: Microsoft Azure - Essentials
Lecture 258 Introduction to Microsoft Azure Essentials
Lecture 259 Five Principles of Cloud
Lecture 260 History of Cloud Computing
Lecture 261 Key Types of Cloud Services
Lecture 262 Cloud Deployment Models
Lecture 263 Why Microsoft Azure
Lecture 264 What is Microsoft Azure
Lecture 265 Microsoft Azure Architecture
Lecture 266 Register for free Azure Account
Lecture 267 Demo- Microsoft ARM Portal
Lecture 268 Demo - ASM Portal
Lecture 269 Enterprise Account
Lecture 270 Azure Subscriptions and Account
Lecture 271 Tools for Azure
Lecture 272 Installing Azure Powershell
Lecture 273 Installing Azure CLI
Lecture 274 Azure CLI -Getting Started
Lecture 275 Azure PowerShell ASM
Lecture 276 Azure Powershell ARM Basics
Lecture 277 Azure Virtual Machines
Lecture 278 What is Azure VM
Lecture 279 Azure VM Status
Lecture 280 Azure VM Billing
Lecture 281 Azure VM sizes
Lecture 282 Azure VM deployment Options
Lecture 283 Azure VM Architecture - Classic
Lecture 284 Azure VM Architecture -ARM
Lecture 285 Azure VM Pricing Tiers
Lecture 286 Create a Windows VM
Lecture 287 Create a Windows VM Continues
Lecture 288 Connecting to a Windows VM
Lecture 289 Create a Linux VM
Lecture 290 Connecting to a Linux VM
Lecture 291 Azure VM Disks
Lecture 292 Attaching Data Disk to a Windows VM
Lecture 293 Attaching Data Disk to a Linux VM
Lecture 294 High Availability in Azure
Lecture 295 High Availability in Azure Continues
Lecture 296 VM Availability Sets
Lecture 297 Creating an Availability Set
Lecture 298 Joining VM into an Availability Set
Lecture 299 Azure Virtual Network
Lecture 300 Azure Network Options
Lecture 301 Components of Virtual Networks
Lecture 302 Address Spaces and Subnets
Lecture 303 Network Security Groups
Lecture 304 Azure Load Balancers
Lecture 305 Azure Load Balancers Continues
Lecture 306 Azure HYbrid Connectivity Options
Lecture 307 Azure Traffic Manager
Lecture 308 Creating a Virtual Network and Subnets
Lecture 309 Creating VM in a VNet and Adding NSG Rules
Lecture 310 Azure storage
Lecture 311 Azure Blob Storage
Lecture 312 Azure Disk Storage
Lecture 313 Azure File Storage
Lecture 314 Azure Table Storage
Lecture 315 Azure Queue Storage
Lecture 316 Creating a Storage Account and Uploading a Blob
Lecture 317 Azure Web Apps
Section 9: Microsoft Azure - Data Lake
Lecture 318 Introduction to Azure Data Lake
Lecture 319 Introduction to Azure Data Lake Continue
Lecture 320 Creating Analytics Account
Lecture 321 Services in Azure Data Lake
Lecture 322 Processing Data Lake Store
Lecture 323 Concept of USQL Language
Lecture 324 Defining Analytics Units
Lecture 325 Adding Filter Operation
Lecture 326 Stages of Job
Lecture 327 Brief on USQL Language
Lecture 328 Extracitng a Row
Lecture 329 Schema on Read
Lecture 330 Aggregating the Data
Lecture 331 Changing the Group By
Lecture 332 Injesting Multiple Files
Lecture 333 Processing the Multiple Files
Lecture 334 Arranging the Data
Lecture 335 Distributing the Data
Lecture 336 Data from Data Lake
Lecture 337 Checking the Status Update
Lecture 338 Creating the View
Lecture 339 Table Valued Functions
Lecture 340 Script for Table Valued Functions
Lecture 341 Creating a Store Procedure
Lecture 342 Running Store Procedure
Lecture 343 How to use Inline cSharp
Lecture 344 Azure Portal to Visual Studio
Lecture 345 Creating New Project
Lecture 346 Services in Azure Account
Lecture 347 Submitting SQL Job
Lecture 348 Analyzing the Job
Lecture 349 Creating Project Function
Lecture 350 Monitoring the Status
Lecture 351 Job Comparison Tool
Lecture 352 Understanding Job Graph
Lecture 353 Understanding Job Graph Continues
Lecture 354 Executing the Job File
Lecture 355 Concept of Heat Map
Lecture 356 Vertex Execution View
Lecture 357 Concept of Job Effeciency
Lecture 358 Options in Diagnostics Tab
Lecture 359 Ways of Accessing Azure Data
Lecture 360 Creating Data Store Accounts
Lecture 361 Confirming Through Portal
Lecture 362 Creating a Directory Structure
Lecture 363 Uploading the Data
Lecture 364 Using the Powershell
Lecture 365 Commands for Azure CLI
Lecture 366 Data Lake Store Account
Lecture 367 Uploading Data with Azure CLI
Lecture 368 Renaming the File
Lecture 369 Deleting Data Lake Account
Lecture 370 Pricing of Azure Data Lake
Lecture 371 Summary on Azure Data Lake

Aspiring Data Scientists: Individuals aiming to build a career in data science and machine learning using Microsoft Azure.,Data Engineers: Professionals involved in designing and managing data processing systems who want to incorporate machine learning capabilities.,AI Engineers: Those interested in developing AI solutions on the Azure platform.,IT Professionals: System administrators or IT managers looking to understand Azure's capabilities for machine learning and data analytics.,Students and Graduates: Those pursuing studies in computer science, data science, or related fields seeking practical knowledge of Azure's data science offerings.,Professionals Transitioning to Data Science: Individuals from non-technical backgrounds looking to transition into data science and AI roles.,Technology Enthusiasts: Anyone passionate about exploring advanced data analytics and machine learning in the cloud environment.

Homepage