Foundations of Cloud Computing: AWS and Azure Essentials
(10 Hours)
This course is designed for software engineers, developers, and technical professionals seeking a strong foundation in cloud technologies across AWS and Azure. It combines core concepts in compute, storage, networking, DevOps, and security with hands-on labs using virtual machines, containers, and serverless tools. As the first step in a 4-course Cloud Engineer program focused on applied AGI development, it equips learners with essential cloud skills to boost their technical careers.
Enroll in AppliedTech Foundations of Cloud Computing: AWS and Azure Essentials Program
This 10-hour foundational course is designed for Software Engineers, Developers, Architects, and technical professionals aiming to build essential cloud skills. As the first course in a 4-part Cloud Engineer program, it introduces core concepts and services across AWS and Azure. Learners explore cloud fundamentals such as virtual machines, containers, serverless computing, storage (block, object, and databases), networking (VPCs and IP subnetting), and more. Each topic is reinforced with hands-on labs and real-world demos to ensure practical understanding.
In addition to infrastructure basics, the course covers key DevOps and security practices, including CI/CD workflows and platform-specific security tools. Through three interactive labs, participants gain confidence in deploying and managing cloud resources in both environments. This course serves as a strong entry point for advanced learning in cloud engineering and AGI development, while also supporting career growth in cloud-native roles.
Key Topics Covered:
Cloud Basics: Virtual machines, containers, and serverless computing.
Storage: Block, object, and managed databases.
Networking: VPCs, IP subnetting, and practical cloud configurations.
DevOps & Security: CI/CD workflows and cloud-native security features.
With three interactive labs and practical exercises, learners gain confidence in cloud environments, laying a strong foundation for advanced cloud and AGI development.
An overview of what you will learn from this program.
Learn how to deploy and manage virtual machines, containers, and serverless computing services in AWS and Azure, with hands-on demos.
Gain practical experience with cloud storage options like block storage, object storage, and managed database services in AWS and Azure.
Understand the fundamentals of cloud networking, including Virtual Private Cloud (VPC) and IP subnetting, with practical demos in both cloud environments.
Get hands-on experience with cloud-based DevOps practices, focusing on continuous integration and deployment (CI/CD) pipelines in AWS and Azure.
Learn the essential cloud security practices, including identity management, encryption, and compliance measures in AWS and Azure.
Engage in multiple hands-on labs to apply learned concepts, including virtual machine deployment, containerization, serverless computing, and database management.
Program Highlights
Hands-on Labs
Code demos and most updated labs to sharpen your skills and practice your learnings. Access latest and powerful LLM models through our online platform and be up-to-date.
Personalized Mentorship
Receive personalized guidance from experienced faculty and mentors, benefiting from a low student-to-instructor ratio that ensures you receive tailored support and assistance.
Experienced Faculty Members
Learn from top industry experts. A low student-to-instructor ratio guarantees close interaction with faculty, enabling a personalized learning experience and effective support.
Enhancing Employability
At Our Academy, mentors develop skilled talent for Industry 4.0 by providing comprehensive support, ensuring you gain the expertise employers need.
Internship Certificates Based on Performance
At AppliedTech, our internship certificates reflect real skills, not just attendance. Every certificate is earned through performance, project work, and practical impact.
About the Foundations of Cloud Computing: AWS and Azure Essentials
Foundations of Cloud Computing: AWS and Azure Essentials
This course is designed for technical professionals such as Software Engineers, Developers, and Architects who want to build a strong foundation in cloud computing. It focuses on core cloud services offered by AWS and Azure, including cloud compute (virtual machines, containers, serverless), storage, networking, DevOps, and security. Through practical demos and hands-on labs, participants will gain real-world experience with deploying cloud infrastructure, managing storage solutions, configuring networks, and implementing CI/CD pipelines. This foundational course will prepare participants for more advanced cloud engineering and AGI development opportunities.
Over the course’s 10 hours, learners will explore critical topics such as virtual machine deployment, cloud storage configurations, object storage, and managed databases. They will also dive into networking concepts like VPC and IP subnetting, as well as cloud security practices essential for safe and efficient cloud operations. Additionally, the course covers cloud-native DevOps workflows, including CI/CD, to help participants integrate modern software development practices into cloud environments. By the end of the course, learners will have a solid understanding of both AWS and Azure cloud platforms, ready to tackle more complex cloud projects.
Why choose AppliedTech :
At AppliedTech, we envision a future where individuals are empowered to navigate and excel in the rapidly evolving realm of technology. Our dedicated team is committed to revolutionizing the learning experience, instilling innovative thinking and adaptability to keep pace with the ever-changing technological landscape.
Our mission at AppliedTech is to cultivate a culture of continuous learning and development, nurturing individuals from diverse backgrounds, whether rooted in the world of IT or branching out into non-IT domains. We firmly believe that knowledge has no boundaries, and we are dedicated to breaking down barriers to make technology education accessible to all.
Get Ahead with Foundations of Cloud Computing: AWS and Azure Essentials Program
Certificate of Completion for the Program
Internship Certificate from Participating Companies
Letter of Recommendation
Program Eligibility Criteria and Prerequisites :
- No programming experience needed
- All tools used in this course are free for you to use.
- Internet, Laptop/PC
- We start from the very basics
Building Foundations of Cloud Computing: AWS and Azure Essentials Course Outline
- Cloud Compute – VMs, Containers, Serverless compute with demos (AWS/Azure)
- Hands-on Labs (3): Virtual Machines, Containers, Serverless Compute
- Cloud Storage – Block, Object store, managed database services with demos (AWS/ Azure)
- Hands-on Labs (2): Managed database service, object storage (AWS/ Azure)
- Cloud Networking & VPC, IP Subnetting with demo (AWS/ Azure)
- Cloud DevOps/ CI-CD with demo (AWS/ Azure)
Cloud Security (AWS/ Azure)
Course Syllabus In Detail :
- Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
- Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
- Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
- Assignments and Interview Questions
- Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
- Session 5: Tuples + Set + Dictionary
- Tuple
- Operations on tuple
- Set functions
- Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
- Assignments and Interview Questions
- Create functions.
- Arguments and parameters
- args and kwargs
- map(), filter(), reduce()
- Assignments and Interview Questions
- Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
- Session 8: OOP Part2
- Encapsulation
- Collection of objects
- Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
- Session on Abstraction
- What is Abstraction?
- Abstract class
- 3 Interview Questions
- Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
- Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
- Session 12: Decorators
- Decorators with Examples
- Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
- 4 Interview Questions
- Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
- Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.
- Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
- Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
- Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.
- Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
- Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
- Session 21: MultiIndex Series and DataFrames
- Session on Pandas Case Study
- Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots
- Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot
- Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
- Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
- Data Preprocessing steps.
- Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL
- Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
- Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.
- Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
- Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
- Mathematical Transformation
- Log Transform
- Reciprocal Transform / Square or sqrt Transform
- Power Transformer
- Box-Cox Transform
- Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
- Session 44: Confidence Intervals
- Confidence Interval
- Ways to calculate CI
- Applications of CI
- Confidence Intervals in code
- Confidence Interval
- Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
- Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
- Single sample t-Test
- Independent 2-sample t-Test
- Paired 2 sample t-Test
- Code examples of all of the above
- Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
- Session on ANOVA
- F-distribution
- One/Two-way ANOVA
- Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
- Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
- Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
- Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?
- Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi supervised Machine Learning
- Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
- Data collection
- Insufficient/Labelled data
- Non-representative date
- Poor quality data
- Irrelevant features
- Overfitting and Underfitting
- Offline learning
- Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
- Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
- Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
- Why we need Polynomial Regression?
- Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
- What is multicollinearity?
- How to detect and remove Multicollinearity
- Correlation
- VIF (Variance Inflation Factor)
- Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
- Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
- Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
- Assignments and Interview Questions
- Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
- Session 5: Tuples + Set + Dictionary
- Tuple
- Operations on tuple
- Set functions
- Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
- Assignments and Interview Questions
- Create functions.
- Arguments and parameters
- args and kwargs
- map(), filter(), reduce()
- Assignments and Interview Questions
- Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
- Session 8: OOP Part2
- Encapsulation
- Collection of objects
- Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
- Session on Abstraction
- What is Abstraction?
- Abstract class
- 3 Interview Questions
- Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
- Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
- Session 12: Decorators
- Decorators with Examples
- Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
- 4 Interview Questions
- Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
- Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.
- Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
- Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
- Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.
- Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
- Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
- Session 21: MultiIndex Series and DataFrames
- Session on Pandas Case Study
- Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots
- Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot
- Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
- Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
- Data Preprocessing steps.
- Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL
- Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
- Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.
- Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
- Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
- Mathematical Transformation
- Log Transform
- Reciprocal Transform / Square or sqrt Transform
- Power Transformer
- Box-Cox Transform
- Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
- Session 44: Confidence Intervals
- Confidence Interval
- Ways to calculate CI
- Applications of CI
- Confidence Intervals in code
- Confidence Interval
- Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
- Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
- Single sample t-Test
- Independent 2-sample t-Test
- Paired 2 sample t-Test
- Code examples of all of the above
- Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
- Session on ANOVA
- F-distribution
- One/Two-way ANOVA
- Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
- Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
- Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
- Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?
- Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi supervised Machine Learning
- Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
- Data collection
- Insufficient/Labelled data
- Non-representative date
- Poor quality data
- Irrelevant features
- Overfitting and Underfitting
- Offline learning
- Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
- Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
- Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
- Why we need Polynomial Regression?
- Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
- What is multicollinearity?
- How to detect and remove Multicollinearity
- Correlation
- VIF (Variance Inflation Factor)
Ready to be a Foundations of Cloud Computing: AWS and Azure Expert?
Enroll in our Foundations of Cloud Computing: AWS and Azure Essentials program at AppliedTech Academy and gain the essential skills to excel in cloud engineering!
As cloud computing continues to drive digital transformation, there’s an increasing need for professionals who can leverage leading cloud platforms like AWS and Azure to build and manage scalable, secure, and efficient infrastructure. This hands-on program offers practical experience with cloud compute, storage, networking, DevOps, and security services. Through a combination of foundational theory, real-world applications, and interactive labs, you’ll develop the expertise to deploy and manage cloud resources effectively. Whether you’re looking to advance your career or enhance your organization’s cloud capabilities, this course will provide the critical knowledge to thrive in today’s cloud-driven world.
Enroll Today!
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Foundations of Cloud Computing: AWS and Azure Essentials FAQs
This course is designed for software engineers, developers, architects, and other technical professionals who want to build or enhance their skills in AI and machine learning.
No prior cloud experience is required. This course starts with the basics and gradually introduces more advanced concepts through guided demos and hands-on labs.
The course covers both Amazon Web Services (AWS) and Microsoft Azure, focusing on their core services in compute, storage, networking, DevOps, and security.
Yes, the course includes multiple hands-on labs covering virtual machines, containers, serverless compute, object storage, and managed databases to reinforce learning.
The total duration of the course is 10 hours, with a balanced mix of theory, demos, and practical lab sessions.
Upon completion, you’ll be able to confidently deploy and manage cloud infrastructure in AWS and Azure, and you’ll be well-prepared for more advanced cloud or AGI development programs.
The course includes approximately 10 hours of instruction and hands-on practice, spread across multiple modules.
The course completion certificate is valid for a lifetime and does not have an expiration date.
To enroll in the Foundations of Cloud Computing: AWS and Azure Essentials Program course, visit the AppliedTech website and complete the enrollment form.
This program equips you with in-demand AI and machine learning skills, opening up opportunities in data science, AI development, and other high-growth tech roles.