Microsoft Azure Mastery: Basics To Advanced Applications

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.