DEV Community: mohit mishra

Hypervisors to Containers: A Dive into AWS Virtualization World🌍

mohit mishra — Tue, 08 Apr 2025 14:13:21 +0000

Have you ever wondered what really happens under the hood when you launch an EC2 instance, install Docker, and run your containers on it ?

Cloud computing often abstracts away infrastructure, but understanding the layers gives you superpowers as a developer, DevOps engineer, or architect.

Most of us start by running a container on a cloud VM, but lets see
how the entire stack is layered.

We will be seeing right from Hardware,Hypervisior, Bare Metal , Cloud, VM, Docker, Pod work together. So Lets jump in.

Let’s break this down in simple terms, from the hardware all the way up to your containers, and answer a fundamental question:
Where exactly does our container live inside AWS?

The Typical DevOps Stack:
🧞‍♂️Build a Docker image using a Dockerfile
🧞‍♂️Launch an EC2 instance (say, Ubuntu)
🧞‍♂️Install Docker or containerd
🧞‍♂️Run your container (e.g., Django, Node.js, etc.)

But what’s the full architecture behind this?

The Layer: Container → EC2 → Hypervisor → Physical Server
Here’s how the stack actually looks:

Container (Your App)
This is your running application, isolated with its own filesystem, environment variables, and dependencies. You can run multiple containers, each hosting your different microservice.
Docker Engine (or containerd)
Below container, this is the runtime that manages your containers.
It handles pulling images, starting/stopping containers, networking, and more.
EC2 Virtual Machine (Guest OS)
This is the Ubuntu or Amazon Linux OS you selected while creating the EC2 instance.
It’s a Guest OS running inside a virtual machine and it’s fully yours to control.
AWS Nitro Hypervisor (The LEGEND)
AWS uses a lightweight bare-metal hypervisor called Nitro.
Unlike traditional hypervisors, Nitro doesn’t sit on top of a host OS.
It runs directly on the hardware, creating isolated VMs with high performance & security.
There’s no general-purpose host OS under the Nitro hypervisor , just a lean hypervisor layer and AWS’s custom hardware modules.
Physical Server (The Actual Machine)
At the very bottom is a real, physical server inside an AWS data center.
The Nitro hypervisor slices this machine into many virtual machines (EC2 instances) for users like us.

Your Container is Actually… 5 Layers Deep
So when you docker run something on your EC2 instance (on cloud), here’s what’s happening:

🤖Our container runs our app (e.g., Django)
☸️But our created container is running on top of…
🐳A container engine(docker),which installed on…
🐧A guest OS, which is running inside…
☁️A virtual machine(EC2),which created by…
💻A hypervisor,which is sitting on real hardware.
You’re not just running a container:
You’re running it inside a container engine, on a guest OS, inside a VM, on a hypervisor, on AWS bare metal.

Like Inception for DevOps 🍰 layers on layers!

Why Should You Care?
Understanding this layering helps you:

Debug better: Know if an issue is in the container, the VM, or deeper.
Architect smarter: Choose EC2 vs Fargate vs Lambda more effectively.
Secure more: Understand isolation between containers, VMs, and tenants.
Ace certifications: Like the CKAD, where container fundamentals matter.
But then a natural question should pop up:

“We know containers share the Host OS.
But I’m also using FROM ubuntu in my Dockerfile , isn’t that an OS too?”

Q: So what’s going on here, are there 2 OS, one of Container and one of Host?

Ans: Yes… but not in the way you might think.

When you use a Docker image like:
FROM ubuntu

You’re not pulling in a full-blown OS ,you’re only getting the userland i.e:

Shell (/bin/bash)
Libraries (libc, apt, etc.)
Utilities (curl, ls, etc.)
What you don’t get is the kernel , that’s always provided by the Host OS.

Let’s Split the Concept of an “OS” into Two Parts:
Kernel-> The core of the OS, responsible for talking to hardware, managing resources, etc.

Userland -> Everything else: shell (bash), utilities (ls, cat, apt), libraries (glibc), and file system structure (/bin, /etc, etc.)

So When You Write:
dockerfile
FROM ubuntu:22.04
You’re NOT packaging the kernel of Ubuntu.

You’re only including:
The Ubuntu userland
Tools and libraries to make your app run like it would on Ubuntu

The container still uses the Host OS’s kernel to make system calls.

So our running CONTAINER, isn’t a full VM, it’s just a lightweight, isolated process that shares the Host’s kernel but has its own filesystem, processes, and networking.

NOTE:
🚀In VM: Each VM has its own full OS (Guest OS), which is a copy separate from the Host OS.
It keeps using system resources even when idle.

🚀In Containers: They share the Host OS kernel.

🚀When no process is running inside a container, it stops (because a container is just a process).

Final Thoughts
Cloud computing often abstracts away infrastructure, but understanding the layers gives you superpowers as a developer, DevOps engineer, or architect.

So next time when you SSH into your EC2 instance running a container, remember: you’re operating five layers above the metal.

Personal Thought:

And If you’re worried about AI replacing jobs or questioning your ability to keep up, remember:
Every expert was once a beginner. 🌱

I have already written blogs on AI/ML with AWS. Check them out if you like.

And please, Stay curious and motivated, keep learning and moving forward. 🚀
Keep Calm, Stay Aware, and Keep Your Chin Up! 😊 You will do it! ✨

If you want any personal suggestions or a one-to-one call with me, I’d be more than happy to chat🌿
Let’s connect on Linkedin for a Hi !! 🤝

Now, take a deep breath 🌬️ and Go Learn! 🌏

AWS #Cloud #AWS #2025 #DevOps #Mindset #Win #NewWorld #Technology #hypervisor #baremetal

Your Most Important Skill for 2025

mohit mishra — Sun, 05 Jan 2025 17:47:27 +0000

Read this article ONLY if you are serious for 2025 else DONT. Its fine.

So, Its 2025 and its already getting older 👴🏻

So which important skill have you planned to learn this year or this month or this week ?

Cloud, Devops, Python,GenAi, Blockchain, JS ??

Now, Its imp to share a note with you about me!!
I am an IT professional with 10yrs of experience and working as a Lead Cloud Architect in a Denmark🇩🇰 based firm.

On a daily basis, I have to deal with below task, read carefully
🔥Devops & CICD pipelines tools like Bitbucket pipeline/cicrleCI & Jenkins, 🔥Managing Kubernetes clusters using Kops
🔥Writting python code in Django creating, updating API’s
🔥Working on AWS cloud infra, different IAM roles & policies.

Now, when i list down these skills and tools which i am working right now, its sound fascinating right ?

But, trust me i have full faith that they wont be in my resume as well in the market in next 5 6 months🤷🏻‍♂️Its a “FACT”

The biggest skill you can learn in 2025 world is “Learn to Learn Fast⚡”

The pace at which the market is changing, its impossible to just know one or two skill and stick to it for years. Those days are gone.

Tools & Languages are getting older in just 2 3 months being in market.

You have to adapt to this rapid changing world. But how ?🤷🏻‍♂️

My 5 tips on Learning “How to Learn Fast” 👨🏻‍💻

It’s all mind game 🧠. Prepare your mind to STOP fearing changes. 💭

The first thing your mind will say are these lines:
“Shit, it’s a new tool, it will take 2–3 months to learn it, it’s not possible what this Team Lead is saying.” 🤯
You have to overcome this fear of change. 💪

When a new tool comes in front of you, just say:
“It’s just a tool, I have experience with something similar. I will learn it quickly.” 🔧🚀

Stop wasting your time on its theory part. Start implementing and:
“Start Fast” to “Fail Fast” ⏩💥
“If you fail late, remember you will pass late as well.” 📆⏳

Follow a proper course, material, community, pages, and writers 📚👩🏻‍💻 to stay aware of what changes are happening in the market. 🌍

It will sound oldy, but trust me, its all about
“Either You Control Your Mind OR the Mind Controls You” 🤷🏻‍♂️

Let me pin down some current hot skills in market:(Acc to my experience)

🔥Artificial Intelligence (AI) and Machine Learning (ML)
Proficiency in AI and ML is highly sought after, as businesses integrate these technologies to enhance operations and customer experiences.

🔥Data Science and Analytics
The ability to analyze and interpret complex data sets is crucial for informed decision-making and strategic planning.

🔥Cloud Computing , Devops , MLOps
Expertise in cloud platforms like AWS, Azure, and Google Cloud is essential as organizations continue to migrate to cloud-based infrastructures with Devops automating things.

🔥Cybersecurity
With the increasing frequency of cyber threats, skills in cybersecurity are critical to protect sensitive information and maintain trust.

But make yourself understand that:
“Everything is possible in this world”
Every skill can be learnt.
Each language can be practised.
Just open your mind and thoughts🔥

FACT CHECK: You Like it or Not, this is a fact that market is changing rapidly.

You have to keep yourself updated.
Accept it and make your mind to accept it too positively 😃

From my side i am trying to decode complex topics like cloud, devops,GenAI, MLOps, security in simple manner by linking them with our daily day-to-day things we do.
Like this one “explaining Docker & Kubernetes using a selfie example 🤳🏻”

So “Learn to Learn Fast”.

Market is changing, so Change your Thinking, on Your Capabilities as well🦾
And You gonna DO IT🧞

Millions of people have done it, why you can’t do it ?
You Will !!🕺🎉

If you felt, i gave you a reallity check.And we are on the same page now.
Do let me know by a clap or a comment🙌
Will be happy, knowing your thoughts on the same💫

And if by any chance, you want to connect with me on Linkedin will be happy to guide or have a discussion, with you there as well🙋

Life is Your Movie & You are not a Chracter in it, YOU are the CREATOR of that Movie💥🌍
Keep pushing, Keep growing.

2025 #learnfast #life #growth #mindset #achiever #targets #technology #cloud #devops #aws #change #update

How AI Understands What I Am Asking? 🤔 “Tokens”- the Language of AI with AWS!

mohit mishra — Wed, 01 Jan 2025 17:31:08 +0000

We hear about AI everywhere these days.

And yes, it stands for Artificial Intelligence , an intelligence created artificially, to help machines do tasks, that would otherwise require human intelligence. 💡

But my first question was : How AI know what I am asking or telling it?Simple answer: AI models read & understand the text we give them.
Actual answer: Tokenization, the language of AI. 🧩

So How AI Reads Text 🧠
We humans have evolved to process words almost instantly.

We no longer focus on individual letters — it all happens rapidly in the back of our minds.
Words come together to form meaningful sentences seamlessly.

AI works similarly but with a twist. When an AI model receives a prompt (a question or instruction from us), it doesn’t see it as full sentences or even just words.

Instead, it breaks the sentence down into smaller units called “Tokens” 🧱

Tokens Are NOT Word Count! 🚫

So what Are Tokens?
Tokens are the building blocks of how AI processes text.
They are not the same as words but are smaller chunks of text.

For example:
The word “Understandable” is split into 3 tokens: “Under”, “Stand”, “Able”.

The question “What is the capital of India?” is tokenized as:
“What”, “is”, “the”, “capital”, “of”, “India”, “?”.

Each word or piece of text is tokenized this way, for the AI to process and understand. 📝

IMPORTANT: Tokens and AI Costs 💰

Here’s where it gets interesting: The number of tokens matters!

AI companies charge based on the total number of tokens you send in your prompt and the AI’s response.

For example:
If your input has 50 tokens and the AI generates a 100-token response, you’ve used 150 tokens total for that interaction.

Companies like OpenAI, Amazon, and others use these token counts to determine their pricing plans. 💵

And Why Tokens Matter 🌟
Understanding tokens helps us better appreciate how AI works and why costs vary.
Every word, punctuation mark, and even a long word broken into smaller chunks adds to the token count.

AI services charge based on the number of tokens processed — both in the input (your question) and the output (AI’s response). Here’s how it works:

Short queries cost less as they use fewer tokens.
Complex or detailed responses use more tokens, increasing the cost.
For instance, if you input 200 tokens and the response is 300 tokens, the total is 500 tokens.
Each service has different pricing models. For example:

GPT-4 might charge $0.03 per 1,000 tokens, so a query with 1,500 tokens would cost $0.045.

Every token matters because, it determines the cost, context limits (how much text the model can handle at once), and even how efficiently the AI performs.

And As Everything is Happening on Cloud, lets explore it in AWS

Tokens in AWS AI/ML Services 🛠️
AWS integrates tokenization across many of its AI/ML services:

Amazon Comprehend uses tokens to process text for tasks like sentiment analysis and entity recognition.
Amazon Lex tokenizes user input to understand intents and manage conversational AI interactions.
Amazon Bedrock enables interaction with foundational models, where efficient token usage can optimize costs.
When using these services, remember: the fewer unnecessary tokens, the more cost-effective and efficient your interactions can be. 💸

So, the next time you chat with an AI model, know that tokens are silently working behind the scenes, making the magic happen. ✨

And If you’re worried about AI replacing jobs or questioning your ability to keep up, remember that every expert was once a beginner.

Stay curious & motivated — keep learning & moving forward.

Keep Calm, Keep Aware, Keep the Chin and Thinking UP !! You will do it !!

If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿
Let’s connect on Linkedin for a Hi !!

Now, Take a deep breathe and Go Learn🌏

From AI🤖 to AWS☁️: A Quick Guide to Essential AI/ML Terms

mohit mishra — Thu, 28 Nov 2024 15:33:03 +0000

Without knowing these Terms, ML/AI will always feel Futuristic/Alien.

In the world of AI/ML and AWS, there’s a sea of terms that often get tossed around.

If you’ve ever felt overwhelmed by them, don’t worry — you’re not alone!

This article will break down the most commonly used terms in AI/ML and AWS, giving you a clear, concise definition of each term, that will bring you one step closer to AI/ML/AWS world.

🔥AI: Its a process of imitating human intelligence in machines.
[problem-solving, reasoning, learning, and understanding language]

🔥ML: A subset of AI, ML refers to the ability of machines to learn from data without being explicitly programmed. It uses “Algorithms to find Patterns” or insights from large datasets.

🔥Deep Learning: A subset of ML, that deals with “algorithms” inspired by the structure/function of the “Human Brain”, called artificial neural networks. [image, speech recognition]

NOTE: When “Learning” comes in picture “Algorithm” comes into picture.
Like in above 2 case, ML & Deep Learning works on ‘Algorithm’

🔥Neural Networks: A set of “algorithms” that attempts to recognize underlying relationships, in a set of data through a process, that mimics the way the human brain operates.
Neural networks are the foundation of deep learning.

🔥Computer Vision: A field of AI that enables machines to interpret and make decisions based on visual data, such as images and videos.
[object detection, image recognition, and face detection]

🔥Natural Language Processing (NLP): A branch of AI that focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language.
[sentiment analysis, text translation, and chatbots.]

🔥Algorithm: A set of instructions or rules, designed to perform a task or solve a problem. In ML, algorithms are used to “train models from data”.

MOST IMPORTANT ONE 👇🏻

🔥Model: A ML MODEL, is a “mathematical representation” of a real-world process.
In ML, it is a function, learned from data, that makes predictions or classifications based on new input data.

A Model is created, by training an algorithm, using data, and the result of this training process is the model.

🔥Training: The process of teaching a machine learning model by feeding it data and allowing it to learn patterns.

🔥Inferencing: The last stage/process of now, using a trained model, to make predictions or decisions based on new data.
Bulk Inferencing: Prediction generated in BULK.DOne in Offline mode.
Real-Time Inferencing: Predicting in real time like self-driving car does.

Dont loose the FOCUS here, just few more terms and you will have a good awarness atleast.

🔥Bias: Bias in AI refers to systematic errors or prejudices that can be introduced in the model due to biased training data or flawed assumptions. This can lead to unfair or inaccurate predictions.

🔥Fairness: Ensuring that AI models make decisions without unjustly favoring or discriminating against specific groups, especially in sensitive areas like hiring, lending, or criminal justice.

🔥Fit: The ability of a model to perform well on the training data.
A “good fit” means the model has learned the underlying patterns, while “overfitting” or “underfitting” may indicate issues in model performance.

🔥Large Language Models (LLM): AI models designed to process and generate human language on a large scale, typically trained on massive datasets.
Used in NLP tasks like language translation, summarization, and conversation generation.

🔥Structured Data: Data that is organized in a specific format or schema, like tables, spreadsheets, or databases.

🔥Unstructured Data: Data that does not have a predefined format, such as text documents, videos, or audio files.

🔥AWS SageMaker: It is a fully managed service that provides developers and data scientists with the tools to quickly build, train, and deploy machine learning models at scale.

🔥AWS Bedrock: It is a managed service that allows you to easily build and scale generative AI applications by leveraging foundation models from leading AI companies.

Okay glad you came this far. I know there must be few confusions, so lets try to know the differences.

Similarities & Differences
🎯AI vs. ML:

AI is the broader concept that encompasses the idea of machines performing tasks that would typically require human intelligence (e.g., reasoning, decision-making).
ML is a “subset of AI” that specifically focuses on using data-driven algorithms to learn from and make predictions based on data.
🎯ML vs. Deep Learning:

Deep Learning is a specialized area within ML.
While traditional ML algorithms, might use “structured data” (like tables)
Deep Learning models are designed to, automatically learn hierarchical features from raw data, especially unstructured data like images and text.
Deep learning typically requires much larger datasets and computing power than traditional ML.
Now, dont loose the momentum, complete the last most important chunk of the blog, “THE MODEL”

🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀

When we hear, “Train the Model”, “Model fine-tuning” etc etc

So How the MODEL Looks ?👀

A model itself isn’t a file you can open (like an Excel sheet).
Instead, it consists of:

The learned parameters (weights and biases): These are the “knowledge” the model has acquired during training.
In linear regression, these would be the coefficients.
In deep learning, these are the weights in the neural network layers.
The architecture: The structure of the model, which defines how the data flows through the model.
For example, a neural network has an architecture with multiple layers, each containing nodes (neurons) connected to each other.

Model as a Saved File:

Once a model is trained, it’s saved into a file. For example:

TensorFlow/Keras: .h5 file
PyTorch: .pth file
Scikit-learn: Pickle file (.pkl)
These files store all the necessary information about the model, such as:

Model architecture (if applicable)
Learned parameters (weights, biases)
Hyperparameters used during training (like learning rate, number of epochs)
Once saved, the model can be used to make predictions on new data without needing to retrain it.

Last part: How Does the Model Make Predictions?
Once trained and saved, the model is ready for inference (making predictions):

You load the trained model from the file.
You feed new, unseen data into the model (like an image of a cat or a house with specific features).
The model uses its learned parameters to output a prediction. For example:
🌾In the case of the house price model, you input the square footage and the number of bedrooms, and the model predicts the house price.
🌾In the case of the image classification model, you input an image, and the model classifies it as “cat” or “dog.”🐈🐶
Summary:

🚀A machine learning model isn’t an Excel file or a chunk of code; it’s a trained mathematical entity (typically stored as a file) that encapsulates learned knowledge.

🚀The model learns patterns from the data during training and is then able to predict outcomes when given new data.

🚀In simple ML models (e.g., linear regression), the model might look like a mathematical equation, while in more complex models (e.g., neural networks), it consists of learned weights and biases stored in specialized formats like .h5, .pth, or .pkl.

=============================
If you’re worried about AI replacing jobs or questioning your ability to keep up, remember that every expert was once a beginner.

Stay curious & motivated — keep learning & moving forward.

Keep Calm, Keep Aware, Keep the Chin and Thinking UP !! You will do it !!

If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿
Let’s connect on Linkedin for a Hi !!

Now, Take a deep breathe and Go Learn🌏

Genai #Cloud #AWS #2025 #devops #mindset #win #newworld #technology

Your ML/AI Success Begins Here: Data Ingestion & Storage on AWS

mohit mishra — Mon, 25 Nov 2024 15:02:21 +0000

Data is the King, but how to treat the King?

Please start this blog, ONLY if you will COMPLETE it.
Else no worries, read it later.

AI, ML, GenAI all these, “Market” as well “Brain” consuming terms are bascially hungry for Data & not just Data but a GOOD DATA.

See it’s simple, you want to stitch a dress, you know you need something sharp, but Needle , Knife, Sword all are sharp.
If you use Sword instead of Needle, its Gone, the task, the work is all gone.

So before jumping into ML, AI, and all, its important to make ourself a bit aware about the DATA.
Data types, its properties and which Tool to be used in which use-case.

It starts with understanding your Data TYPES:

🌾Structured Data: Well-organized in relational databases.
🌾Semi-Structured Data: Loose schema like JSON or XML.
🌾Unstructured Data: Freeform formats like videos or images.

Depending upon the type of data you have, you will choose your Aws Tools accordingly.

🌾Structured: Amazon RDS, Redshift.
🌾Semi-structured: DynamoDB, S3.
🌾Unstructured: S3, Glacier.

See, this what an Architect’s work is, to select proper tools and services.
Else either you will mess up the project flow, or project cost or entire project.

Its like, when you want to travel somewhere, you dont just focus on going from A to B. You see many factors like confort, train , bus, distance, money etc.

So once Data TYPE is covered, quickly lets move to Data Properties.
As its one of the important part as well, that will make you decide which tools to select.

Key Data Properties can be divided into the “3 V’s”
— — — — — — — — — — — —

🌾Volume: The scale of your data its in Mb, Gb, Tb
Small datasets? Use RDS or DynamoDB.
Massive datasets? Go for S3 or Redshift.
🌾Velocity: The speed of data generation, means the speed in which you are getting the data.
Real-time ingestion? Use Amazon Kinesis.
Batch processing? Use AWS Glue or S3 pipelines.
🌾Variety: Diversity in the data formats.
Diverse formats? S3 is your versatile solution.
You see, how data , its type , its properties are creating a initial blueprint of your project.
These steps is what in later future, will help you in MLOps.

Now depending upon the above factors, you will decide which Storage you will use, but there is Types of STORAGE as well.

Data Storage Architectures:
— — — — — — — — — — — — — —

Data Warehouses
Its a Centralized storage, optimized for “structured” data & analytics.
For Scenerios like Reporting, business intelligence (BI) or structured queries.
AWS Service: Amazon Redshift.
Key Feature: Schema-on-write, ensuring organized and consistent data.
Data Lakes
Its a Scalable storage for “structured, semi-structured, and unstructured data”. Its like a dumping ground.
For scenerios like Big data analytics, ML model training.
AWS Service: Amazon S3.
Key Feature: Schema-on-read, enabling flexibility for data scientists.
Data Lakehouse
Its a hybrid model combining the best of data lakes and warehouses.
For scenerios like Unified analytics and ML pipelines.
AWS Service: Amazon Redshift Spectrum, Athena (for querying S3 data).
Key Feature: Seamless integration between structured and unstructured data.

I wont dig more, as i know , for an initial step & for an initial awarness this much info is fine. We will slowly, dig down step by step, in further steps how AWS handles ML,AI, GenAI projects, process.

So just remember, without bread, you cant make a sandwich.
And without data & knowing the data, you cant bring a ML project in-life.

If you’re worried about AI replacing jobs or questioning your ability to keep up, remember that every expert was once a beginner.

Stay curious & motivated — keep learning & moving forward.

Keep Calm, Keep Aware, Keep the Chin and Thinking UP !! You will do it !!

If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿
Let’s connect on Linkedin for a Hi !!

Now, Take a deep breathe and Go Learn🌏

Genai #Cloud #AWS #2024 #devops #mindset #win #newworld #technology

The Silent Revolution: How GenAI is Reshaping Cloud Tech☁️

mohit mishra — Sat, 11 May 2024 11:19:11 +0000

Lets explore how GenAI is conquering world and creatings its own world on the infra of Cloud.

In the ever-evolving IT world, terms like Cloud, DevOps, and MLOps frequently surface. But the latest buzzword is GenAI.

So, what exactly is Generative AI?

Traditional AI can distinguish between objects — train a model with 1000 of cat and dog photos, and it’ll identify them accurately.

GenAI takes this a step further by creating something new.
Imagine a photo of a cat with the body of a dog, that’s GenAI in action.
It’s not just descriptive, it’s generative.

With advancements in data processing, model training, and computational power, GenAI is now poised to revolutionize various industries by generating new content like text, images, and audio that mimic human-created content.

GenAI leverages deep learning models, particularly those based on architectures like Generative Adversarial Networks (GANs) and Transformer models (like GPT for text and DALL-E for images), to produce outputs that are novel and realistic.(Just like above image)

Okay now, GenAI is with us and we can see it getting used in our day to day life as well. But to cook something you need vessel, to drive a car, you need road.

Same way to keep the GenAI alive and thriving, there must be a place, an infra where it is kept, stored, worked upon and trained.
That place is our friendly CLOUD.

And It has to be cloud as it’s clear the cloud’s vast storage and computing capabilities are vital.
In essence, the cloud is the backbone that supports and nurtures the growth of GenAI technologies.

If every field is using or trying to use GenAI, wont cloud with 100 of service, & being backbone of GenAI, will try to use GenAI? Ofcource Yes.

Implications of GenAI in the Cloud Domain
— — — — — — — — — — — — — —

As a Cloud Architect, integrating GenAI within your AWS environment can significantly boost innovation and efficiency:

🚀Innovation and Development
🌾Automating Content Creation: Use AI to automatically generate reports, documentation, marketing material, or user-specific content.
🌾Enhancing User Experience: Implement AI-driven chatbots or virtual assistants that provide human-like interactions for support and services.

🚀Enhancing Cloud Services:
🌾Personalization: GenAI can analyze user behavior and generate personalized recommendations for services or products.
🌾Security: Use AI to generate realistic cybersecurity simulations or training data, enhancing threat detection systems.

🚀Optimizing Operations:
🌾Resource Management: AI can predict workload trends and automatically adjust resources, optimizing cost and performance.
🌾Maintenance: Predictive maintenance can be enhanced with AI, predicting failures and automating routine checks without human intervention.

Other then these factors, there are so many GenAI tools as well present in AWS cloud.

GenAI Tools on AWS

AWS provides several tools and services that facilitate the use and deployment of Generative AI models:

🚀Amazon SageMaker: A comprehensive service to build, train, and deploy machine learning models at scale. It supports the latest AI frameworks, making it ideal for developing GenAI applications.
🚀AWS DeepComposer: Allows developers to get hands-on with Generative AI, particularly in music generation.
🚀AWS DeepRacer: Provides an interesting way to learn about reinforcement learning, a type of AI that could be considered under the broader umbrella of generative techniques.
🚀AWS DeepLens: Enables developers to integrate AI into applications using a deep learning-enabled video camera.

So in conclusion, GenAI is the hottest thing in our IT world, which is being nurtured & taken care in our Cloud world.

MOST IMPORTANT : A Friendly note for me and for you as well :
— — — — — — — — — — — —

These 2 tech, will for sure change how we are functioning at the moment.

Dont loose your mind, in thinking
“I will be laid off, Ai will replace me, i cant learn, i dont have skill”.

Embrace this change. Learning and adapting to new technologies like GenAI is a step-by-step journey.
If you’re worried about AI replacing jobs or questioning your ability to keep up, remember that every expert was once a beginner.

Stay curious and motivated — keep learning and moving forward.

Keep Calm, Keep Aware, Keep the Chin and Thinking UP !! You will do it !!

If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿
Let’s connect on Linkedin for a Hi !!

Now, Take a deep breathe and Go Learn🌏

Genai #Cloud #AWS #2024 #devops #mindset #win #newworld #technology

The ONLY 4 Skills You Need to Master Devops/Cloud

mohit mishra — Fri, 05 May 2023 00:54:44 +0000

The moment we hear Devops or Cloud, an army of tools and skills comes infront of us🤷🏻‍♂️
But do we really need to learn those 50 60 skills to call yourself a Devops or Cloud Guy🧐?

Devops and Cloud are the hottest things in the IT market at the moment🔥
But with this, there comes a mountain of tools we hear like Docker, Jenkins, Graffana, Prometheus, Kubernetes, ArgoCD, Bamboo, Helm…+100 others !!🕵🏻‍♂️

Being in the IT industry for 9 years & working as a Lead Cloud Architect, let me be honest with you all, these tools are needed in one way or another👨🏻‍💻

BUT BUT…🤚🏻
🤷🏻‍♂️Do I actually need to know all this?
🤷🏻‍♂️Do I use all this ?
🤷🏻‍♂️Do i learn all this, to be called a Devops or CLoud Professional ?
HELL NO !! HELL NO !!🙅🏻‍♂️

Every 3 or 4 months, there is a list of new tools coming in the market🙇🏻‍♂️
It’s impossible to get your hands dirty on all those tools and most importantly it’s also not possible that your team will be using all of them.

For example, for CI/CD one company can use Jenkins, while another can use Bitbucket CICD Pipeline or another must be using Circle.ci or another can be using AWS CICD pipeline🤌🏻

Even in AWS cloud, there are tons of services and those who are creating those services, are even not aware or using all of them, because obviously IT’S NOT POSSIBLE to know everything😅

So now, let’s Only Focus on the Skills that are crucial and important, to be aware of and these skills won’t be getting replaced in just a month or two, they’re the foundation on which this Devops/Cloud world is created other then legacy skills like Linux,Networking and all🌐

⚡CLOUD:
Devops will be needing cloud as a platform🛤️
Its like if you want to catch a train, you need a platform where train will come.
Same way all the Devops tools, will be needing Cloud as a platform, so its important you make yourself good in a cloud like AWS or Azure or GCP☁️
Skill: Stephen Mareke Udemy : AWS Cloud Practioner + AWS Solution Architecture Associate

⚡DOCKER:
The code that you write to create your Project and docker is used to create a Docker Image of your project & which when we run, creates a container for us, inside which our Project is running. So its a gateway to Devops world🐳

⚡KUBERNETES:
The docker image, when we run, creates Containers in which our project run📦
There can be 100 of such containers and to handel these containers we should know Kubernetes, which is a Container Management Tool.
Resources: Youtube- TechWorld With Nana :Kubernetes Tutorial ☸️ , Udemy : Mumshad and rest all his course on Kubernetes like CKAD, CKA or CKS.

⚡TERRAFORM:
Now this is a latest addition in the list of hot thing in market🔥
This bascially helps you to create things(servers) on cloud by using scripts in Yaml, rather then doing login in AWS or Azure and doing click click click.
I have written a blog explaining Terraform using a Movie example🎥
And to crack Terraform Certification you only need 2 things:

Terraform Course by Mumshad on Udemy or by Zeal Vora
Paper Practise Teast by Bryan

Thats All….!!!🎉🥳🙋🏻‍♂️
And trust me, things you listen other then these 4 like CICD, Graffana & many others are useful ONLY when you know these 4 or atleast First 3 Skills🧞‍♂️

So remove all confusions from your mind, clear your thoughts and just make a 6 month target and tick-off these skills one by one📝👊🏻🤟🏻

You can build a Building only when you have a strong Foundation💪🏻
Once Foundation is done, its upto up you wana make it of 4 floor, 10 floor or a skyscraper🏯

If you feel, I made sense in a positive & clear way, do let me know either here or lets connect on Linkedin 🙋🏻‍♂️
https://www.linkedin.com/in/mohitmishra3333/

And please, lets come out of this jungle of tools every HR or Job Description has created🙅🏻‍♂️

Don’t waste your money and time blindly roaming around this jungle of Tools 🧑🏻‍🦯
Use this list and go ahead from many around you🎉
And you will do it, I know, even You know it !!🦁

If you want any personal suggestion or a one-to-one call with me, will be more then happy to have one🌿🌾
Let me know on Linkedin !!
Now, Take a deep breathe and Go get it🌏

AWS #Docker #Kubernetes #Terraform #Devops #Skills #Roadmap

Terraform by a Movie example🎦

mohit mishra — Fri, 13 Jan 2023 19:13:54 +0000

Lets decode this highly demanding skill of 2023 in Cloud/Devops world🌎

As always we will take a layman example to understand this concept.
Let’s take an example of Movie Watching 🎦🍿🎥

You can watch a Movie in 2 ways:

Open your door, take your car, go to theater, buy a ticket & watch🫡
Be at home, watch it on Netflix,Prime Video😎

NOTE: Let’s keep our focus on the end result of you watching a movie.
In both ways, you will be able to achieve the task.

The Second type is what Terraform is.Let me explain how😲

In the cloud, we need to create various infrastructures like servers, DB, Security groups,VPC & 100 like them.

Again, we have 2 ways of creating it, same like watching a movie:

Normal way: Open your respective Cloud Login page, do all the authentications, search which service you want,click click click click and lots of click🥵
Terraform way: Write a script in YAML with all your requirements & RUN…boom Done 🚀
Just like the movie you watched is owned by the Netflix company.
Terraform is owned by HashiCorp company🏭

Also just like on Netflix, you can watch all the movies present in it.
Using Terraform, you can create infra on any Cloud like AWS,GCP + 2780 as they have collaborated with Terraform🥳

By having connection with so many Cloud Providers, terraform helps us to be cloud platform independent & gives us freedom to be multi-cloud☁️☁️

And all this is done, by writting declarative code in a .tf file & when we run it, our Cloud Infrastructure is created🧞‍♂️

That’s why Terraform is called “IaaC tool (Infrastructure as a Code)”⚙️

Note: Terraform is of declarative type, meaning you just write/declare what you want as End result & it will take care of how to do it.
Like create 2 aws server or 3 DB, 2 pods. So it’s not imperative, it’s declarative.

Lets understand now, how this magic happens🪄🔮🧙🏼‍♂️

In any team, a Developer will create a project and a Devops/Cloud guy will create the needed infrastructure on cloud to run that project smoothly on cloud.

In above example, we have used “AWS” as “Provider” & its different Resources like ami,instance_type for creation.

So once script is ran, we can login to our AWS account & we will be able to see that a ec2 server instance is running in our AWS account.
To connect your Aws account, just configure AWS CLI access credentials of your AWS account on your local machine.

Architecture of Terraform:

Now Terraform Architecture, can be divided into 2 set:

⭐[SCRIPT,STATE,CORE]
The “script/config file” you write, is the result you desire & “State” is the actual state at present.Core of terraform creates a execution PLAN & takes care that ‘Desired’ state = ‘Actual’ state by creating, updating or deleting things.

⭐[POVIDERS]
The Cloud “Providers” like AWS, Azure have tied up with Terraform are 2nd component of Terraform architecture⚓

“AWS” being a “Provider” for Terraform, we can use all AWS Resources like we can create an AWS EC2 server, then deploy Kubernetes cluster on that server then create services inside that cluster.

Just like AWS, we have 100 more such Cloud Providers, that brings their 1000 resources with them.

For example, below you can see we have used “Kubernetes” as a “Provider” & are using Kubernetes different Resources to create a Namespace.
(My Blog on Namespace using a cupboard example)

Terraform Commands for Different Stages:

terraform refresh: to know the condition of current state, later to be matched to desired state.
terraform init: initialize terraform directory, pull down providers as written by you in .tf file here its AWS.
terraform plan: creates plan for making Desired state == Current state
terraform apply: execute the plan created by above step.
Also as now our Infrastructure is written in a script, we can maintain athe versions of this script by storing it in git just like we do for our project codes. And changes can now be tracked down as who did the change in infra and also will be needed approved by raising PR to a Team Lead , thats why if there is any issue on Infra comes, we can roll back to any paticular earlier version.

So at the end Terraform is just like our Netflix, where we can see any movie without actually going into theaters & we have options for multi-watching.

Same way Terraform helps us to create infra on any cloud without logging into its portal & just by writting a chunk of code and it gives us option to go multi-cloud.

Have given Terraform Certified Associate exam and below is my Cert⭐

We have just scratched the surface, but Thank you for being till here with me, if you feel u have learnt something new, let me know for sure, it will means a world to me 🙌🏻

And if you share same interest as me in decoding these complex topics in an easy way them lets connect🙋🏻‍♂️

Learning Networking in AWS Cloud using a simple Hotel example

mohit mishra — Thu, 05 Jan 2023 16:01:55 +0000

There are few terms you must have heard like cloud, region, vpc, subnets, internet gateway, NAT.

All these terms are related to only one thing i.e having our own private space for our project in cloud.

Lets take a layman example to connect these dots.

Suppose you want to book a room in a hotel.
So we will go to a particular HOTEL, which is in a specific region and in a specific area of a country.

Similarly when we select a cloud provider like AWS, Azure or GCP they are like hotels present in a region in various availability zone(AZ).
So we can select region like central europe and zone like eu-central-1a.

Now you talk to receptionist and he/she allocates a room with a room number like 101.

In cloud world, its like you are booking a VPC(Virtual Private Cloud) in AWS could with an address range like 10.0.0.0/16.(Now it’s your own private space)

As we are inside our Hotel Room and if we want to connect to outer world, we will connect to internet through hotel’s WIFI.

Similalrly inorder to connect our VPC to internet/outerworld we need Internet Gateway.

Inside the VPC we booked, we create small small subnet in it and we decide which machine/process to be placed in which subnet.

And Subnets are like windows in our room in hotel, some are open to outerworld like window in hall from where you can call someone passing by.
But some are private like window in washroom. Outerworld is not accessible from there.

Similarly Subnet are also

Public subnet and Private subnets created inside our VPC.
We can keep our webserver in public subnet so tht anyone on internet can connect to our website directly(but thats risky).

And we can keep our DB, Codes, imp files in private subnet which should not be open to internet.

But what if you want to keep everything in locker(private), means we want to put our webserver in private subnet as well for security.

Then comes Load Balancer which we place between the “Internet” and our “subnet” , that allows users to access our webserver placed in private subnet.

But if we put our webserver in private subnet and cut it out from outerworld, there will be a time when, even our webserver need to connect to internet for some kind of installations or updations.

So we use NAT (Network address translation) which is placed inside a “public subnet” in our VPC which allows our webserver present in “private subnet” to pass its outgoing request first to NAT which will pass it to internet gateway and then to internet.

There also can be scenario where our customer say i have my VPC at my end and i dont want to move to public cloud providers like AWS or Azure.
Then we use VPC Peering Connection, which will connect customer VPC to our VPC in AWS.

We can have multiple such VPC and we have to connect these vpc to on-premises vpc as well, and if we go vpc peering on set , every vpc has to be connected to one to one vpc , because vpc peering are non-transmitive then it becomes a blockage.

Thats why, in 2018 AWS launched a new Networking Router called as “Transit Gateway". It allows to connect different VPC’s in different network as well our on-premise network VPC.

So now using a vpn or direct connection we can connect our on-premise vpc to Transit gateway, then Transit gateway will connect to our AWS VPC. This creates a complete connectivity.

Now there are other AWS services also like S3 bucket and DynamoDB if they are in same region, we don’t need to go through subnet and all, as it will cost us more money as well.

We can use VPC endpoint(gateway) for S3 and DynamoDB and for other services like Lambda, SQS, SNS we can use VPC Endpoint(interface) for direct connectivity rather then going through NAT.

In another case, what if a customer wants only you to access their app first before they open it for public on internet.
Then comes “PRIVATE LINK" which will get the customer application’s link through their NLB in their VPC, and then it will pass the link to our VPC.

Customer can expose their application’s link via their NLB and at our end we can create a VPC endpoint, which will privately connect us to the NLB of the customer.👏

Also if we dont want an IP address for our VPC, we can use Route53(DNS) so that users can connect to us using the “domain name” rather then the ip address through our ALB.

There is also Cloudfront, which will cache our website general data, big videos, images to nearest AWS AZ, so when user hit our domain name it will go to Cloudfront and then to our LB and finally to the application in our subnet. This makes faster user experience.

If you connected a bit by my post, do let me know and if we both share same intrest of devops,cloud & kubernetes then lets connect!!

Linkedin: https://www.linkedin.com/in/mohitmishra3333/
Medium: https://medium.com/@mohitmishra3333

Happy Learning to all !!

Most Important Skill for 2023 & 2024

mohit mishra — Thu, 05 Jan 2023 11:08:03 +0000

Its 2023 and its already one day old now 👴🏻

Which important skill have you planned to learn this year or this month or this week ?

Cloud, Devops, Python, Blockchain, JS ?

Let me share a note with you about me!
I am an IT professional with 9 yrs of experience and working as a Lead Cloud Architect in a Denmark based firm.

On a daily basis, I have to deal with Devops & CICD pipelines tools like Bitbucket pipeline/cicrleCI & Jenkins, Managing K8s clusters using Kops, sometimes writting python code in Django framework for projects, working on AWS cloud infra and different IAM roles and policies.

When i list down these skills and tools which i am working right now, trust me i have full faith that they wont be in my resume in next 5 6 months.

The biggest skill you can learn in 2023 is “Learn to Learn Fast⚡”

The pace at which the market is changing, its impossible to just know one or two skill and stick to it for years. Those days are gone.

Tools & Languages are getting older in just 2 3 months being in market.

You have to adapt to this rapid changing world. But how ?

My 5 tips on Learning “How to Learn Fast” 👨🏻‍💻

Its all Mind game. Prepare your Mind to STOP Fearing from Changes.

The first thing your mind will say to you is “Shit its a new toll,it will take 2 3 months to learn it, its not possible what your Team Lead is saying”. You have to Overcome this fear of change.
When a new tool comes infront of you, say “It’s just a tool, i have experience of something simillar like it. I will learn it quickly.”
Stop wasting your time on its theory part.Start implementing and start to Fail Fast.
“If you Fail Late, remember you will Pass Late as well.”
Follow proper course, material, community, pages, writters to be aware what changes are happening in market.
It will sound oldy, but trust me, its all about “Either You Control Your Mind OR the Mind Controls You” 🤷🏻‍♂️

_Let me pin down some current hot skills in market:(Acc to my experience)
_
🔥Cloud: Go for AWS as it has max market share, so more openings,more rejections,more selections chance.
🔥Blockchain Developer: Learning JS will be helpful.If not in Blockchain then as a developer its in full demand as well.
🔥Devops: Docker,Kubernetes,CICD tools(Bitbucket,CircleCi,Jenkins),Terraform.
🔥Cybersecurity: Network security monitoring tools(Argus, Nagios, Pof, Splunk, and OSSEC),Encryption tools(Tor, KeePass, VeraCrypt, NordLocker, AxCrypt, and TrueCrypt),Web vulnerability scanning tools(Burp Suite, Nikto, Paros Proxy, and SQLMap) and other concepts.

But make yourself understand that:
“Everything is possible in this world”
Every skill can be learnt.
Each language can be practised.
Just open your mind and thoughts🔥

*FACT CHECK: You Like it or Not, this is fact that market is changing rapidly.
*
You have to keep yourself updated.
Accept it and make your mind to accept it too positively 😃

From my side i am trying to decode complex topics like cloud, devops, security in simple manner by linking them with our daily day-to-day things we do.
Like this one explaining Docker & Kubernetes using a selfie example 🤳🏻
https://dev.to/mohit0303/learning-docker-kubernetes-orchestration-using-a-selfie-example-8bm

So “Learn to Learn Fast”.

Market is changing so should be our thoughts, for Your Capabilities.All the best.

Will be happy knowing your thoughts on the same.

Medium: https://medium.com/@mohitmishra3333
Linkedin: https://www.linkedin.com/in/mohitmishra3333/

Keep pushing, Keep growing. #2023 is yours.

Explaining Cloud using a Pendrive example

mohit mishra — Thu, 05 Jan 2023 11:02:13 +0000

Next time in an interview if you are asked “What is Cloud?”
You can explain the concept using this simple Pendrive example🥳

First let’s Understand “Why Cloud” using a simple Mobile example📱

Let me explain by a scenerio.
Today by chance if you broke your mobile📱(I wish it doesn’t happen), will all your data be lost ? “NO”🙅🏻‍♂️

Because, the moment you buy a new mobile & login in it by your apple ID or gmail id, all your data get restored 🥳

It means your all photos,contacts and other detail bascially were not stored in your phone. They were at some different place.

Then where it was ? They ‘were’ and ‘are’ present in CLOUD☁️

Now “What is cloud?” lets understand it by a Pendrive example🙋🏻‍♂️

Lets go back 10 years from now, if we want to take a movie from our friend , we used to give him our pendrive.
We connect it to our computer and access the movie🍿🎦

But, if dont want to share the movie with your brother, you will hide that pendrive in a very safe private place🕵🏼‍♀️
But there is an issue, now whenever you want to watch the movie, each time you have to go to that secret place and this is a tedious work🥵

Cloud is exactly like this “Pendrive kept at a Safe Secret Place” and by using internet we can connect to that secret place from being anywhere in the world🌎🥳

All the gmail, google photos, contacts & our every important details are stored in a machine in a safe place by companies like Google, Apple & by using internet we connect to our data using our id &password🌐

Now, to store these huge data, there are bascially acers of lands used, where the DataCenters factories are setup, with all storage device & infrasturucture required to keep our data safe there🏭

These secret places are present in many countries across the globe🌎

Just like our personal photos and other personal details, we can even keep our Projects, Code, Database too in them💻👨🏻‍💻🙌🏻

This facility of Cloud Service is given by many companies like AWS, Azure, GoogleCloudPlatform, Alibaba, IBM and many others.

When we create an account in any of these Cloud Service Providers, they provide us with the secret place where we want to store our data.🕵🏼‍♀️

These places are called Region in AWS, and we should select that Area in a Region which is actually nearby to us.

Like if you are in Europe, we have option of Frankfurt, Ireland, London, Paris, Stockland.🌎

These Areas in a Region are called “Availability Zones” & there can be one or more then one AZ in a Region.

And thats all regarding the concept & presence of Cloud☁️

And for the next time, just sock the interviewer about what is cloud 😎 🕺🏻
I hope you got a bit clearity & closer about the concept, if Yes then do let me know your thoughts.

And if you share same intrest as me then lets connect to break down complex topics into an easy day-to-day life examples.🙋🏻‍♂️
Happy Learning !!

Medium: https://medium.com/@mohitmishra3333
Linkedin: https://www.linkedin.com/in/mohitmishra3333/

Learning Docker, Kubernetes, Orchestration using a selfie example🤳🏻

mohit mishra — Thu, 05 Jan 2023 10:39:50 +0000

Docker , Kubernetes, Orchestrations let me try to explain these things using selfie example🙋🏻‍♂️

Using our mobile camera we take selfies, selfies get stored in mobile gallery🤳🏻
Likewise using docker we take selfies of our project & store them in our machine💻

If your selfies are not run/used, they are just using your mobile space 👾
But when you use/run your selfie, you get a new DP on whatsapp, FB🕺🏻

When you run docker image, you get a new container 🗳️

Now to handle your so many selfies, we have google photos 🧑🏻‍🎨
To maintain so many containers, we have Kubernetes ☸️

So, let’s revise again

Using Docker as a camera, you take a docker image of your code.🖼️
When you run the docker image, you get a container ☸️
And Kubernetes helps in deleting, updating, and maintaining these 100’s of containers🗳️🗳️🗳️

Kubernetes helps in creating a Cluster, inside the Cluster we have our machines/nodes inside which our created containers are running.

Now what is Orchestration? Lets take a cricket game example 🏏

Example of Orchestration:🎼

On hotstar, in a cricket match, in the last 2 3 overs, number of viewers can increase by 10x 📈
So, to suddenly handle these loads of requests, k8s will autoscale or create 100 such more containers🗳️🗳️🗳️🗳️

When match is over, we close the app, so now the number of requests is reduced📉
K8s deletes the newly created containers as they are not needed now 🗳️❎

This feature gives auto scaling, high availability, no down time for our application.

And thats all

Just remember Docker is our CAMERA , Docker Image is our Selfies, running these selfies we get Container like our DP, Kubernetes is Google Photo app which helps to maintaine these selfies i.e the containers.

In the next part will try to explain #kubernetes #architecture in layman term

If you connected a bit by my post, do let me know and if we both share same intrest of devops,cloud & kubernetes then lets connect!!

Linkedin: https://www.linkedin.com/in/mohitmishra3333/
Medium: https://medium.com/@mohitmishra3333

Happy Learning to all !!