AWS Essentials - Part 1 [ Introduction to AWS ]

AWS Essentials

Amazon Web Services is a leading cloud-based services platform and if you are aiming for a career in cloud technology, AWS Essentials is a great place to start. This course will introduce you to the various services and products offered by AWS.

Introduction to AWS

Welcome to this course on AWS Essentials!

Here you will first learn about Cloud Computing and its characteristics. Then you will understand What is AWS, its application and its usage. Following that, you will learn in detail about the different kinds of products AWS supports.

1. Compute
2. Storage
3. Database
4. Networking and Content Delivery
5. Developer Tools
6. Management Tools
7. Security and Compliance
8. Application Services
9. Messaging

AWS Cloud

Here you will get a chance to know more about the different offerings of AWS.

What is Cloud Computing?

Before drilling down further into AWS, let us first understand What is Cloud computing?

Cloud Computing

• It is the type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand.
• It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services) provisioned with minimal management effort.
• This relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid over an electricity network.)

Source - Wikipedia

Cloud Characteristics

National Institute of Standards and Technology (NIST) highlights various characteristics that are needed for a service to be regarded as “Cloud”.

1. On-demand self-service - Sign up and enjoy the services without delays.
2. Broad network access - Access service through standard platforms (laptop, mobile, desktop, etc.)
3. Resource pooling - Resources are pooled to multiple customers.
4. Rapid elasticity - Ability to meet demand peaks.
5. Measured Service - Billing is metered and delivered as a utility service.

Three unique categories within Cloud Computing:

• Software as a Service (SaaS)
• Platform as a Service (PaaS)
• Infrastructure as a Service (IaaS)

Software as a Service (SaaS)

1. Capability to run applications on a cloud infrastructure.
2. Applications are accessible from several client devices via either a thin client interface, like a web browser e.g., web-based email
3. The interplay between the outside world and organization. e.g., email newsletter campaign software
4. Software for a short term requirement. e.g., collaboration software for a particular project
5. “Vanilla” offerings where the solution is not differentiated.
6. The consumer does not control or manage the underlying cloud infrastructure, which includes servers, networks, operating systems and storage.

SaaS is not suitable in scenarios where the application

1. processes quick real-time data.
2. has a regulation or legislation that does not allow data to be hosted externally.
3. the existing on-premise solution that satisfies all of the requirements of an organization.

Platform as a Service (PaaS)

Computing platform that permits creating web applications effortlessly, fast, with no complexity of buying or maintaining the infrastructure and software.

1. Services to develop and test applications, as well as deploy, host and maintain applications in a similar integrated development environment.
2. Ability to deploy on cloud infrastructure with the help of programming languages, services, libraries, and tools.
3. Built-in scalability of deployed software with failover and load balancing.
4. Integration with databases and web services through common standards.

PaaS is not suitable in scenarios where the application -

• requires to be more portable concerning where it is hosted.
• performance needs customization of the underlying software and hardware.
• proprietary approaches or languages would affect the development process.

Infrastructure as a Service (IaaS)

Capableness to provide networks, processing, storage, and other fundamental computing resources, and ability to deploy, run arbitrary software that can include operating systems and applications. Here, the consumer is incapable of controlling or managing the underlying cloud infrastructure.

1. Resources are distributed as a service and enable dynamic scaling
2. Utility pricing model
3. Multiple users on one hardware Applicability-
4. New organizations with less capital could invest in hardware easily
5. Organizations growing rapidly
6. Pressure on the organization to restrict capital expenditure and to migrate to operating expenditure

IaaS is not suitable in cases where

• strict regulatory compliance is followed
• A very high level of performance is required.

More on Cloud Computing

Let's discuss some advantages to moving to the cloud

1. Variable as opposed to upfront
2. Fixed cost
3. Economics of scale can reduce Operating cost
4. It's easier to match capacity to demand
5. It allows you to focus on developing and deploying applications instead of the undifferentiated heavy lifting associated with managing an on-premises data centre.
6. It allows you to increase the velocity of your Agile development and allows a global presence right out of the gate.

Types of Cloud Services

Following are the different types of services that are offered by cloud

1. Infrastructure as a Service: This allows you to easily provision the IT components you require; including networking capabilities, computers, multi-tenant or dedicated, and data storage. It's flexible and allows you to control and manage your IT resources similar to the way you would in a traditional on-premises data centre, such as EC2, S3, and VPC.
2. Platform as a Service: It frees you from having to manage the underlying infrastructure and focus on the deployment and management of your application. It frees you from having to think about resource procurement, capacity planning, software maintenance, and patching. Examples of Platform as a Service on AWS include Route 53, Elastic Load Balancing, and Auto Scaling.
3. Software as a Service: It provides you with an application that is run and managed entirely by a service provider. Think of SaaS as an end-user application running in the cloud. In a SaaS environment, you have access to the capabilities of an application without the hassle of how it's maintained or its underlying infrastructure

Geographical displacement

Next, let's take a look at the infrastructure in terms of its geographical dispersement.

• Regions are geographical areas, such as California, that contain multiple data centres in what is called availability zones.
• Availability zones are separate physical data centres that may exist within a particular region but have separate infrastructure dependencies, such as the electrical power grid, flood plain, and any other factors that might isolate it from the potential of outages
• In addition to AZs, AWS supports numerous Edge locations. Throughout the globe, there are a lot more Edge locations than there are AZs. And these are small kinds of point of presence services used to deliver content, such as the Cloud Front and Content Distribution Network.
• Infrastructure usage is the idea that you pay only for what you use, such as EC2. EC2 has various options, one of which is on-demand, which is where you only pay for the instance as long as you're using the instance or it's running.
• Pricing concepts include paying for infrastructure usage, such as when using EC2; and data usage and transfer, such as when using Amazon S3 or DynamoDB.
• Designing for high availability is a stricter requirement than designing for fault tolerance. For example, in architecture with a single instance, with an auto-scaling group of one, an instance failure will heal itself or replace the instance since the rule might say, "always provide one instance." This is an example of fault tolerance and not high availability. If however, I have an auto-scaling group with two instances in different AZs and one fails, the traffic will automatically route to the second instance. This is an example of higher availability.
• Global infrastructure services include Identity and Access Management (IAM). Core services include networking, computing, storage, and databases, Application services include SNS, SQS, and SWF, Deployment and management services include Elastic Beanstalk and Cloud formation.

History of AWS

Extending IT Infrastructure to the AWS Cloud

Observe how the IT extends further to support Cloud computing in this video

Features of AWS

AWS offers numerous ways to create and manage resources. Following are the different ways to access the features offered by AWS.

AWS Management Console - A web interface for AWS.

AWS Command Line Interface (AWS CLI) - Commands for a wide set of AWS products.

***Command Line Tools***- Commands for individual AWS products.

AWS Software Development Kits (SDK) - APIs that are specific to programming language or platform.

Query APIs - Low-level APIs that are accessible using HTTP requests.

You will learn more about AWS Management Console and CLI in detail as you progress.

 AWS Management Console

AWS Command Line Interface

The AWS Command Line Interface is a unified tool that manages several AWS services from the command line and automates all the services through scripts.

AWS-shell is a command-line shell program to offer productivity and ease features to aid advanced and new users of the AWS Command Line Interface.

Key Features Include:

• Fuzzy auto-completion for Resource identifiers, Options, Commands.
• Dynamic in-line documentation
• Execution of OS shell commands
• Export executed commands to a text editor

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Basic Programming in Python 3

Whitespace - Indentation

At this point, you may be wondering how Python knows which statements get grouped for if the statements while loops and function definitions and languages like C++ C and Java they use braces Python does not have any syntax for braces in MATLAB, you have the end a statement that indicates the end of a block of code in Python all of this is determined by indentation let's take a look at a couple of examples let's say X is some value in this case we see that the indentation puts the print high a statement within the code block so executing this code no matter what the value of x is will always print hello but only if X is true we'll high get printed so let's look at a slightly more the complicated example we again have a print high and then we have print hello and then aligned with this we have a print high again, in this case, these two are not aligned properly so this is maybe two spaces and this is one space here and this will cause an error in Python everything has to be aligned within a given code block now this syntax is okay and we get the same type of indentation for code blocks with functions and while loops everything is just denoted by indentation.

Read, Evaluate, Print, Loop

Hello everyone, in this article we will look at the Repple in Python. so, what is ripple stand for ripple is basically read evaluate print and then go back again to read so what does it mean it means that the shell environment in Python is an interactive environment and whatever we type into Python and when we hit enter it reads that then it evaluates whatever we have said that needs to be done and then it prints back the results to us and then it loops back to the read mode where it waits for us to give more input to Python so that it can again evaluate and print so it goes in this roundabout fashion where it reads evaluates prints and then again goes back to read so you can also go ahead and read this on Wikipedia I will leave a note for this in the in the video notes so I'll leave the hyperlink for this and if you want you can further read this let's just go and play of it with Python to understand what ripple is so let's type Python so this repple have started so this is the first print where it tells us the version and what what version of python we are using so for example if we type 5 plus 3 and hit enter python reads that evaluates it and then prints the feedback sorry prints the result said to us and then loops back to the read mode again so if i type 5 in to say 6 it has again read it evaluated at printed and looped back to the read state we can also assign variables like y is equal to 8 and that stays in the memory it has read it and then we can say why into five where it will evenly evaluate the result of fine to five and print it back to us like 40 we can also use underscore underscore basically helps his point to the last variable assignment and I can say into five it will give us four oh so 40 was the last result set and I have be used it and if I do again in to underscore into five it becomes a thousand set underscore basically refers to the last results that we've had or the last print and we can reuse that something which is an anomaly here is for example the print statement because there is nothing to evaluate if I say hello world and hit enter it just prints so in this case it reads there is nothing to evaluate but it prints and then it loops back to the read now he will just quit Python here and move back to our command prompt so I'm in Windows I will just type control Z and then hit enter and it brings me back to my Windows prompt so in this video we learned about the repel or how Python works interactively in a shell environment I hope you find this video useful and thanks for watching this video please do subscribe to my channel if you enjoy what I am doing Thanks.

Hands-on - Practice Question PYTHON 3

1. Print

Greeting Quote

Mr Greet is the name of the digital notice board placed at the entrance of the seminar hall. Its purpose is to welcome each and every participant of the seminar by showing a welcoming quote with their name on it.

It is based on the function ‘Greet’ which takes a single parameter ‘Name’ as a String are the names of the participants as Input One by one.

Write the function definition for ‘Greet’ which will generate the welcoming quote as below :

For Example, the ‘Name’ is “Ramakrishnan” then the welcoming quote will be :

Output:

Welcome, Ramakrishnan.

It is our pleasure to invite you.

Have a wonderful day.

 

Note:

‘Name’ must be of String Datatype.

 

Input Format for Custom Testing:

It’s a single line containing a name.

Sample Test Case 1:

 

Sample Input

STDIN      Function
-----      --------
Karthik →  Name

Sample Output

Welcome Karthik.
It is our pleasure inviting you.
Have a wonderful day.

Installing Python 3 in Windows and Linux by www.BlueTEXT.in

Introduction

Python is a widely used high-level programming language first launched in 1991. Since then, Python has been gaining popularity and is considered one of the most popular and flexible server-side programming languages.

Unlike most Linux distributions, Windows does not come with the Python programming language by default. However, you can install Python on your Windows server or local machine in just a few easy steps.

Prerequisites

• A system running Windows 10 with admin privileges

• Command Prompt (comes with Windows by default)

• A Remote Desktop Connection app (use if you are installing Python on a remote Windows server)

Python 3 Installation on Windows

Step 1: Select Version of Python to Install

The installation procedure involves downloading the official Python .exe installer and running it on your system.

The version you need depends on what you want to do in Python. For example, if you are working on a project coded in Python version 2.6, you probably need that version. If you are starting a project from scratch, you have the freedom to choose.

If you are learning to code in Python, we recommend you download both the latest version of Python 2 and 3. Working with Python 2 enables you to work on older projects or test new projects for backward compatibility.

Note: If you are installing Python on a remote Windows server, log in via Remote Desktop Protocol (RDP). Once you log in, the installation procedure is the same as for a local Windows machine.

Step 2: Download Python Executable Installer

1. Open your web browser and navigate to the Downloads for Windows section of the official Python website.

2. Search for your desired version of Python. At the time of publishing this article, the latest Python 3 release is version 3.7.3, while the latest Python 2 release is version 2.7.16.

3. Select a link to download either the Windows x86-64 executable installer or Windows x86 executable installer. The download is approximately 25MB.

Note: If your Windows installation is a 32-bit system, you need the Windows x86 executable installer. If your Windows is a 64-bit version, you need to download the Windows x86-64 executable installer. There is nothing to worry about if you install the “wrong” version. You can uninstall one version of Python and install another.

Step 3: Run Executable Installer

1. Run the Python Installer once downloaded. (In this example, we have downloaded Python 3.7.3.)

2. Make sure you select the Install launcher for all users and Add Python 3.7 to PATH checkboxes. The latter places the interpreter in the execution path. For older versions of Python that do not support the Add Python to Path checkbox, see Step 6.

3. Select Install Now – the recommended installation options.

For all recent versions of Python, the recommended installation options include Pip and IDLE. Older versions might not include such additional features.

4. The next dialogue will prompt you to select whether to Disable the path length limit. Choosing this option will allow Python to bypass the 260-character MAX_PATH limit. Effectively, it will enable Python to use long path names.

The Disable path length limit option will not affect any other system settings. Turning it on will resolve potential name length issues that may arise with Python projects developed in Linux.

Step 4: Verify Python Was Installed On Windows  

1. Navigate to the directory in which Python was installed on the system. In our case, it is C:\Users\Username\AppData\Local\Programs\Python\Python37 since we have installed the latest version.

2. Double-click python.exe.

3. The output should be similar to what you can see below:

Note: You can also check whether the installation was successful by typing python –V in Command Prompt. The output should display your installed version of Python. In our case, it is “Python 3.7.3.”

Step 5: Verify Pip Was Installed

If you opted to install an older version of Python, it is possible that it did not come with Pip preinstalled. Pip is a powerful package management system for Python software packages. Thus, make sure that you have it installed.

We recommend using Pip for most Python packages, especially when working in virtual environments.

To verify whether Pip was installed:

1. Open the Start menu and type "cmd."

2. Select the Command Prompt application.

3. Enter pip -V in the console. If Pip was installed successfully, you should see the following output:

Pip has not been installed yet if you get the following output:

'pip' is not recognized as an internal or external command,

Operable program or batch file.

If your version of Python is missing Pip, see our article How to Install Pip to Manage Python Packages on Windows.

Step 6: Add Python Path to Environment Variables (Optional)

We recommend you go through this step if your version of the Python installer does not include the Add Python to PATH checkbox or if you have not selected that option.

Setting up the Python path to system variables alleviates the need for using full paths. It instructs Windows to look through all the PATH folders for “python” and find the install folder that contains the python.exe file.

1. Open the Start menu and start the Run app.

2. Type sysdm.cpl and click OK. This opens the System Properties window.

3. Navigate to the Advanced tab and select Environment Variables.

4. Under System Variables, find and select the Path variable.

5. Click Edit.

6. Select the Variable value field. Add the path to the python.exe file preceded with a semicolon (;). For example, in the image below, we have added ";C:\Python34."

7. Click OK and close all windows.

By setting this up, you can execute Python scripts like this: Python script.py

Instead of this: C:/Python34/Python script.py

As you can see, it is cleaner and more manageable.

Step 7: Install virtualnv (Optional)

You have Python, and you have Pip to manage packages. Now, you need one last software package - virtualnv. Virtualnv enables you to create isolated local virtual environments for your Python projects.

Why use virtualnv?

Python software packages are installed system-wide by default. Consequently, whenever a single project-specific package is changed, it changes for all your Python projects. You would want to avoid this, and having separate virtual environments for each project is the easiest solution.

To install virtualnv:

1. Open the Start menu and type "cmd."

2. Select the Command Prompt application.

3. Type the following pip command in the console:

C:\Users\Username> pip install virtualenv

Upon completion, virtualnv is installed on your system.

Installing Python 3 on Linux

This document describes how to install Python 3.6 or 3.8 on Ubuntu Linux machines.

To see which version of Python 3 you have installed, open a command prompt and run

$ python3 --version

If you are using Ubuntu 16.10 or newer, then you can easily install Python 3.6 with the following commands:

$ sudo apt-get update
$ sudo apt-get install python3.6

If you’re using another version of Ubuntu (e.g. the latest LTS release) or you want to use a more current Python, we recommend using the deadsnakes PPA to install Python 3.8:

$ sudo apt-get install software-properties-common
$ sudo add-apt-repository ppa:deadsnakes/ppa
$ sudo apt-get update
$ sudo apt-get install python3.8

If you are using other Linux distribution, chances are you already have Python 3 pre-installed as well. If not, use your distribution’s package manager. For example on Fedora, you would use dnf:

$ sudo dnf install python3

Note that if the version of the python3 package is not recent enough for you, there may be ways of installing more recent versions as well, depending on you distribution. For example installing the python3.9 package on Fedora 32 to get Python 3.9. If you are a Fedora user, you might want to read about multiple Python versions available in Fedora.

Working with Python 3

At this point, you may have the system Python 2.7 available as well.

$ python

This might launch the Python 2 interpreter.

$ python3

This will always launch the Python 3 interpreter.

Setuptools & Pip

The two most crucial third-party Python packages are setup tools and pip.

Once installed, you can download, install and uninstall any compliant Python software product with a single command. It also enables you to add this network installation capability to your own Python software with very little work.

Python 2.7.9 and later (on the python2 series), and Python 3.4 and later include pip by default.

To see if pip is installed, open a command prompt and run

$ command -v pip

To install pip, follow the official pip installation guide - this will automatically install the latest version of setup tools.

Note that on some Linux distributions including Ubuntu and Fedora the pip command is meant for Python 2, while the pip3 command is meant for Python 3.

$ command -v pip3

However, when using virtual environments (described below), you don’t need to care about that.

Pipenv & Virtual Environments

The next step is to install Pipenv, so you can install dependencies and manage virtual environments.

A Virtual Environment is a tool to keep the dependencies required by different projects in separate places, by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but, Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.

For example, you can work on a project which requires Django 1.10 while also maintaining a project which requires Django 1.8.

So, onward! To the Pipenv & Virtual Environments docs!

Introduction to Python in simple Terms by www.BlueTEXT.in

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. This course is designed for software programmers who need to learn the Python programming language from scratch.

Introduction to Python

Python is a high-level, interpreted, interactive and object-oriented scripting language that finds its application in many areas like -

• Web scripting.
• 3d Modelling (Blender).
• Desktop Applications -` Games (Pygame).
• Scientific usage (SciPy/NumPy).

Python source code is available under the GNU General Public License (GPL). There are two major Python versions, Python 2 and Python 3.

Python features

• Open Source and Simple to use.
• Very powerful and Ubiquitous.
• Supports broad standard library.
• Supports interactive testing and debugging.
• Established interface with all major DB's.
• Runs on a variety of hardware platforms.

Technical features of Python

• Object-oriented (supports both functional and structured programming)
• Dynamically and strongly typed
• Whitespace delimited (Indentation)
• Scripting language which supports large applications.
• High-level dynamic data types and supports dynamic type checking
• Automatic garbage collection
• Interpreted makes compiler interact with a developer.
• Easy integration with C, C++, COM, ActiveX, CORBA and Java.

Python Implementations

CPython - Python implementation on standard C language.

Jython - Python implementation with Java virtual machine to blend with Java.

Pypy - Python implemented in Python and its Just-in-time compiler making it fastest.

Iron Python - for windows, which implements common runtime libraries to interface with. NET.

Difference between Python2 & Python3

Comparison Parameter	Python 2	Python 3
Year of Release	Python 2 was released in the year 2000.	Python 3 was released in the year 2008.
“Print” Keyword	In Python 2, print is considered to be a statement and not a function.	In Python 3, print is considered to be a function and not a statement.
Storage of Strings	In Python 2, strings are stored as ASCII by default.	In Python 3, strings are stored as UNICODE by default.
Division of Integers	On the division of two integers, we get an integral value in Python 2. For instance, 7/2 yields 3 in Python 2.	On the division of two integers, we get a floating-point value in Python 3. For instance, 7/2 yields 3.5 in Python 3.
Exceptions	In Python 2, exceptions are enclosed in notations.	In Python 3, exceptions are enclosed in parentheses.
Variable leakage	The values of global variables do change in Python 2 if they are used inside a for-loop.	The value of variables never changes in Python 3.
Iteration	In Python 2, the xrange() function has been defined for iterations.	In Python 3, the new Range() function was introduced to perform iterations.
Ease of Syntax	Python 2 has more complicated syntax than Python 3.	Python 3 has an easier syntax compared to Python 2.
Libraries	A lot of libraries of Python 2 are not forward compatible.	A lot of libraries are created in Python 3 to be strictly used with Python 3.
Usage in today’s times	Python 2 is no longer in use since 2020.	Python 3 is more popular than Python 2 and is still in use in today’s times.
Backward compatibility	Python 2 codes can be ported to Python 3 with a lot of effort.	Python 3 is not backwards compatible with Python 2.
Application	Python 2 was mostly used to become a DevOps Engineer. It is no longer in use after 2020.	Python 3 is used in a lot of fields like Software Engineering, Data Science, etc.

Print:

• Python 2 treats “print” as a statement rather than a function.
• Python 3 explicitly treats “print” as a function.

Integer Division:

• Python 2 treats numbers without any digits. (Output of expression 3 / 2 is 1, not 1.5). To get the result 1.5, you would have to write 3.0 / 2.0.
• Python 3 evaluates 3 / 2 as 1.5 by default, which is more intuitive for new programmers.

List Comprehension Loop Variables: Common name for the variables that are iterated over in a list comprehension as a global variable get interchanged. This is fixed in Python 3.

Unicode Strings: By default Python 3 stores strings as Unicode unlike Python 2.

Raising Exceptions: Python 3 requires a different syntax for raising exceptions.

• Python 2:raise IOError, “some error message”
• Python3: raise IOError(“some error message”)

What is the Role of a - Machine Learning Engineer

This course outlines the role's details of a Machine Learning Engineer. You will understand the practical on-the-job details of the role.

ML Engineer

What is a typical day in the life of a Machine Learning Engineer?

1. ML Engineers work more on the engineering and less on the science part of the data problem
2. They work on
• Data Extraction
• Data Cleansing
• Model Building
• Model Deployment

ML Engineer Role

Can you explain with an example?

Say, for example, the business wants to develop an application that can classify the user sentiments on the customer's Twitter handle and create some charts based on the same. An ML Engineer would be doing the following:

• Creating the interface from Customer's Application to Twitter API
• Extracting Data from Twitter API
• Cleaning the Data
• Building a Sentiment Classification Model
• Deploying the Model to Customer's Ecosystem
• Maintaining the application

Big Data & DevOps

Should an ML Engineer have any knowledge of Big Data and DevOps?

Yes, most of the big enterprises maintain and archive their data in a Big Data ecosystem. Knowing how to pull the data and work on top of it will help an ML Engineer.

Do ML Engineers need to know DevOps concepts? If yes, to what extent should they be familiar?

Yes, Absolutely. DevOps has become the de-facto term nowadays. ML Engineers should be able to

• Create CI / CD pipelines
• Write Automation Test Scenarios for the application they have developed
• Monitor the application developed
• Containerize the application

ML Engineer vs Data Scientist

Where do we draw a line between ML Engineer and Data Scientist?

Data scientists work more on the Math and Stats part to develop and fine-tune the algorithms required for a given business scenario. ML Engineers take the model and bring them to life by productionizing them.

Technology

What would be an ideal technology that an ML Engineer should be proficient in?

There is no ideal technology but the developer community is embracing Python. Depending on the customer's needs, one has to familiarize a specific technology. Conceptually, developing the application and deploying it into an ecosystem is similar for many technologies.

Technical Expectations

What am I expected to Learn and Know to become a Machine Learning Engineer?

From a Technology standpoint, you should know the following Libraries in Python to perform ML activities.

• Numpy and Pandas - To perform Data Cleansing and Exploratory Analysis
• Matplotlib - To perform Visualizations
• NLTK - To perform NLP
• scikit learn - For Shallow Learning Algorithms
• flask - To develop APIs

In this journey, you are going to explore all these areas and build upon your skills to become a good Machine Learning Engineer.