Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

If you're feeling overwhelmed with how fast AI is evolving, you're not alone.

Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind.

But the good news is that you don’t need to learn everything all at once. What you need is structure.

So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable.

💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition.

If you're moving faster or slower based on time or experience, that’s okay too.
And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here.

👇 Here’s the roadmap:
🔖 Level 1: GenAI & Transformer Foundations
 Tokens, embeddings, transformers, decoding, and inference with open-weight models.

🔖 Level 2: Prompting & Language Model Behavior
 Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting.

🔖 Level 3: Retrieval-Augmented Generation (RAG)
 Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation.

🔖 Level 4: LLMOps & Tools
 LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data.

🔖 Level 5: Agents & Agent Frameworks
 Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation.

🔖 Level 6: Memory, State & Orchestration
 Vector and symbolic memory, episodic vs persistent state, memory compression.

🔖 Level 7: Multi-Agent Systems
 Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams.

🔖 Level 8: Evaluation & Reinforcement Learning
 LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops.

🔖 Level 9: Protocols & Safety
 MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates.

🔖 Level 10: Build & Deploy
 FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens.

📌 Bookmark this.
🛠️ Build something after every level.

And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will  teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 

💻 Pro-tip: Start with cookbooks!

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Hundreds of AI minds gathered in Central Park on a chilly Saturday morning, it was incredible :)

The past weekend in New York felt like one of those moments where community completely surprises you in the best way.

I was traveling to the city for a work event, and my friend Pranjal and I had a thought- What if we host a small AI meetup on Saturday morning in Central Park? Nothing fancy, just a space for people to connect.

So we put up a quick Luma link and shared it on LinkedIn. We expected maybe a handful of people. Instead, the sign ups crossed 450+. 

We put a small table with coffee, muffins, cookies and some cool swag- thanks to Teachable. We weren’t sure how it would go. We just hoped people would show up.

What happened next honestly moved me.

We reached the park at 9.20am to set up for a 10am meetup, and people were already there. In the cold. On a Saturday morning. People had traveled from Boston, Maryland, upstate New York, New Jersey, even Philadelphia. I kept talking to folks thinking, you really came all this way… for this?

The meetup was meant to last two hours. People stayed until 3pm! Almost six hours of conversations about AI, careers, ideas, and everything in between. No microphones. No agenda. Just humans connecting because they care about the same things.

This experience reminded me of something I tend to forget- community doesn’t need perfect planning. It needs intention. It needs genuine people. And it needs a space where everyone feels welcome.

I’m grateful to every single person who showed up. You made this morning unforgettable, and you reminded me why I love building in this space.

Shout-out to Aditya Suresh and Pranam Daga for helping us with putting this together ❤️

There is a quiet moment every November when life slows down just enough for reflection. I never grew up celebrating Thanksgiving. Back home in India, I didn’t even know what it meant. But after moving to the US, watching my friends celebrate, watching sitcoms where families gathered around food and gratitude, I started understanding the deeper meaning behind it.

A moment to pause.
A moment to look around.
A moment to look back.

This year, gratitude feels a little different for me. A little heavier in the best possible way.

I feel grateful for a life where I get to wake up every day and do work I genuinely love. AI is chaotic and intense and fast. Yet it gives me energy. It gives me purpose. And I feel lucky to be part of this ecosystem during a time when everything is shifting.

I feel grateful for the people who hold me steady.
A mom who pushed me when the world expected her to play it safe. A mom who defied every norm to carve space for me to choose ambition without apology.

A husband who is involved in my life in the most beautiful way supportive is honestly an understatement. He is a true partner in every single thing I do. He listens to every idea, every frustration, every new plan I dream up and he backs me with a kind of belief that strengthens my own. Chinnu amma and Chinnu appa who patiently listen to my ten year entrepreneurial plans and cheer for me with the kind of warmth only family can give.

Mentors and colleagues who let me thrive creatively who challenge me in the right ways who make every room I walk into feel lighter and more possible.

I feel grateful for health. For a body I get to take care of. For the time I carve out to move, to eat well, to nurture myself. It took years to realize that taking care of your body is also taking care of your dreams.

And I feel grateful for the little girl I used to be the one who sat with a notebook and wrote her goals on every page the one who kept believing she would get there someday. If I could talk to her today, I would tell her that she is doing great and that her stubborn belief in herself was worth it.

Thanksgiving may not be something I grew up with, but it has become a reminder to pause and appreciate the rarest gift of all the good people around you. People who are genuinely supportive, genuinely positive, and genuinely rooting for you.

I am grateful for all of it.
And grateful for everyone who has been part of my journey so far.

If you are reading this, I hope you take a moment to celebrate the smallest wins, the unexpected joys, and the people who make your life feel expansive.

li_at

JSESSIONID

What an electrifying afternoon! Absolutely delighted to have joined the Mayfield AI List honorary lunch at the beautiful Golden Gate Club in San Francisco.

The room was filled with the brightest minds in the AI VC world. it was a privilege to meet and listen to inspiring leaders like Navin Chaddha, Aparna Chennapragada and Garry Tan. 

I had the opportunity to host a focused roundtable, diving deep into the current state of the industry. We tackled the crucial discussion around Perception vs. Reality of AI, analyzing current hype cycles, and zeroing in on investment opportunities that hold real long-term potential.

It’s moments and small-group settings like these, where leaders can candidly discuss where the real value lies beyond the buzz, that drive the industry forward.

Huge thanks to the Mayfield and Startup Grind team (special shoutout to James Gee and Fadia Frangie Doumat) for hosting and for the award ♥️

No alternative text description for this image

Wrapped up AI Dev 25 hosted by DeepLearning.AI

More than 1400 AI developers gathered in one space, and the energy was something you could feel instantly. Meeting so many builders and hearing what you’re creating reminded me why this community moves the needle the way it does.

Hearing Andrew Ng talk about the future of work was a standout moment. The way he encourages everyone on his team to code, regardless of background, is exactly the kind of mindset shift this era needs. AI literacy is becoming a baseline skill, and he articulated that perfectly.

Our panel with Michael Kearns, Hatice Ozen, Kay Zhu, and Josh Gordon covered how the ecosystem is evolving across the stack, from Groq , Google, and Amazon Web Services (AWS)  to Fireworks AI and how teams like Genspark are using RFT on Fireworks AI in production workflows. We also touched on multi-agent systems, practical best practices, and where developer tooling still needs to mature.

I also loved my conversation with Michael Kearns on responsible AI and how that thinking has evolved from classic ML days to the generative era. His perspective at the intersection of academia and industry always leaves me thinking differently.

Grateful for the conversations and connections today. If we met, thank you for stopping by. If we didn’t, I’m always happy to keep the discussion going. There’s a lot to build, and we’re still early.

Thank you DeepLearning.AI team for having me ❤️

When you’re an AI engineer, the worst bottleneck isn’t writing code. It’s navigating everything around the code.

Last week I tried out Rovo Dev from Atlassian and it really changed how I approach dev work, plus how I see AI agents changing the landscape of software engineering.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗼𝘃𝗼 𝗗𝗲𝘃?
RovoDev is an AI agent built to live inside your dev workflow, including your code, your issues (let’s say in Jira), your docs (let’s say in Confluence), your pull requests. 

It helps you move from idea → code → deployment with fewer context switches.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁:
→ I asked in plain English: “What does this module do? Where are its dependencies?” and got a clear breakdown, no digging through 3 dozen files.
→ I linked my GitHub/Bitbucket + Jira and was able to surface relevant issues, test failures and comments in one context instead of switching tools.
→ The CLI experience (yes, in my terminal) allowed me to trigger agents to draft tests, summarise PRs, or suggest refactoring ideas, without leaving my code. 

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀?
What I realized after using Rovo Dev is that the real time-sink in engineering isn’t writing code, it’s in fact more about re-orienting yourself.

Jumping between Jira, Confluence, and your repo just to understand context.
Trying to figure out why something was built the way it was.
And then losing your train of thought before you can even start solving the problem.

Well, that's where Rovo Dev actually helps close that gap.

If you’ve ever joined a new project and spent hours just piecing together what’s going on, or if you constantly find yourself switching between tools to get one small thing done, you should try this out: fandf.co/42MTjr8 

Thank you Atlassian for partnering with me on this post, and giving me access to Rovo Dev to test it out :)

#AtlassianPartner

This is one of the best resources for aspiring AI engineers, that covers all the basics - advanced in classroom teaching: https://lnkd.in/dgr5zEve

Highly recommend saving this so you can come back to it later.

Goldmine for aspiring AI Engineers

Lately, I’ve noticed something interesting in the AI startup world.

The ones growing the fastest aren’t building the next big model.

They’re building agentic systems.
AI that doesn’t just talk, it does.
Copilots that summarize meetings.

Agents that predict churn or handle internal ops automatically.

These aren’t demos anymore. They’re real products driving revenue.

What separates the ones that scale from the ones that stall comes down to one thing : Data.
Every great agent lives and dies by how it manages memory, retrieval, and reasoning speed.
And that’s exactly why so many founders I work with build on MongoDB Atlas.

It’s not just a database.
It’s the data layer that brings structured, unstructured, and vector data together so your AI can actually reason in real time.

If you’re a startup building AI that acts, not just talks, go check out the MongoDB Startup Program: https://fandf.co/493VsD1

#MongoDBPartner

🚨Context Engineering for GenAI Workshop happening tomorrow!

In this session, we’ll dive deep into:
→ What context really means in LLMs and how it shapes performance

→ The toolkit that helps you engineer it better: Summarization, Pruning, Query Rewrite, RAG, Context Offloading, Isolation, and Memory

→ How to optimize for accuracy, cost, and reliability across use cases

→ A live demo showing how these ideas come together in real time

This workshop is for every engineer who wants to move beyond prompt tuning and start thinking like a system designer.

🧩 Context Engineering
📅 November 8
🕐 1.5 hours
🎙️ with Arvind Narayanamurthy & me

🎟️ Save your spot: https://lnkd.in/dvFmMiRr

If you’re an AI engineer today, you should absolutely get familiar with Model Context Protocol (MCP) and Responses API. These two are becoming the backbone of modern agentic systems.

Let’s unpack why 👇

1️⃣ MCP: The Connector Layer

Model Context Protocol (MCP) standardizes how models connect to external tools, APIs, and live data.

Think of MCP as the wiring system that allows your LLM to talk to the outside world, like tools, databases, vector stores, or your company APIs.

When you build or host your own MCP server, you’re giving your model new abilities to reason, act, and fetch context beyond what’s in its prompt.
This is what turns a plain LLM into an agent.

If you’re working on production-grade AI systems, you’ll often need to:
→ Integrate custom tools or data sources
→ Maintain context across multiple steps
→ Ensure reliable tool invocation and streaming

MCP makes all that possible.

2️⃣ Fireworks AI Responses API

Once your MCP setup is ready, the Responses API is how you connect it to the model.

It’s a next-generation SDK that powers conversational AI. It supports:
→ Stateful conversations (previous_response_id)
→ Real-time streaming for interactive apps
→ Tool calling and safe execution

The Fireworks Responses API follows the same schema and format as OpenAI’s Responses endpoint.

So if you’ve been using OpenAI models, you can switch to open-source models on Fireworks AI with almost zero code change.

You’ll unlock faster inference, lower latency, and broader model choice, including open models like Qwen, Mistral, Llama, and Gemma hosted natively on Fireworks AI.

You can even mix and match:
✅ Use MCP to extend model capabilities
✅ Use Responses API to orchestrate reasoning, tool use, and streaming

That’s the magic combo powering many of today’s most advanced agentic systems.

3️⃣ Why This Matters

Agentic systems aren’t built with just a model call.
They’re built with context, memory, and actions. MCP and the Responses API make that orchestration reliable, stateful, and production-ready.

If you’re serious about building enterprise-grade AI apps, start experimenting with:
→ MCP Examples (connecting to MCP servers and invoking tools)
→ Conversation Chaining (using previous_response_id to continue dialogue)
→ Streaming Output (for responsive real-time interfaces)
→ Ephemeral Mode (for privacy-friendly, non-persistent interactions)

Happy Building 🚀
〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re an AI engineer or an AI leader looking to level up your skills in LangChain and LangGraph, this one is for you.

I’m teaming up with Arvind Narayanamurthy  for a beginner–intermediate, 1.5 hour hands-on workshop where you’ll learn how to move from simple prompts to real AI applications and agentic workflows.

Why this matters
- Most engineers know how to prompt. Few know how to orchestrate.
- LangChain and LangGraph are becoming the backbone of production AI systems.
- If you want to build or lead AI engineering teams, you need these mental models.

What you’ll learn
- Foundations of LLM orchestration and where LangChain fits
- Essential building blocks: chat models, prompting patterns, structured outputs, LCEL
- Tool calling with MCP adapters
- RAG basics end to end
- LangGraph workflows and agentic architectures
- Two live labs: a micro-app and a simple agent
- Quick dive into LangSmith for debugging

The workshop is recorded, and everyone gets the replay.

If you want practical, real-world skills to build modern AI systems, this is the one to sign up for.

Grab your seats: https://lnkd.in/eW2WZdcf

Are you wondering where the AI industry is actually headed? 
Every week brings a new framework, a new toolkit, a new acronym. But beneath all the noise, one truth is becoming clearer: the real moat isn’t the model, it’s the combination of data and AI. 
 
AI is the toolkit. 
Data is the substance. 
AI gives us reasoning power. 
Data gives us understanding. 

That connection came to life at Snowflake BUILD 2025, where the  focus wasn’t on hype, it was on grounding AI in reality. 
 
We saw how Snowflake Intelligence lets teams ask complex questions in natural language and get trusted answers from governed data. How the Cortex Agents API enables developers to build domain-specific agents that reason and collaborate. And how the Managed MCP Server securely bridges external AI systems to enterprise data, turning “agentic AI” from an idea into something production-ready. 
 
Even unstructured data, the 80 % of information hidden in PDFs, chats, and documents, is now first-class and accessible through simple AISQL queries. 
 
This feels like a turning point. 
The next era of AI won’t be defined by bigger models, but by deeper integration between data and reasoning. 
 
AI without data is just potential. 
Data without AI is just storage. 
Together, they become intelligence. 
 
#SnowflakePartner #SnowflakeBUILD

If you’re an AI engineer trying to understand agentic systems, keep reading 👇

A single-agent system relies on one model to do everything: reasoning, planning, execution, and validation. It’s efficient for simple, sequential tasks but quickly breaks down as workflows grow more complex or interdependent.

A multi-agent system, on the other hand, distributes intelligence across specialized agents that collaborate. Each has its own role, memory, & context - and together, they solve problems that a single model can’t handle alone.

But here’s what most people miss: there’s no standard architecture.

A multi-agent system is like Lego blocks, you design it for your use case.

You can build:
→ a supervisor agent managing sub-agents
→ a hierarchical setup with layered control
→ or a hybrid model combining centralized planning and peer coordination
The challenge isn’t in building agents, it's in managing how they talk to each other.

Three reasons multi-agent systems often fail:

❌Memory fragments across agents.
Each agent keeps its own context. When one agent hands work to another, subtle details get lost or duplicated. Without a shared memory layer or context synchronization, agents start “forgetting” what the others already know.

❌Operational costs multiply.
What costs $0.10 for a single agent might cost $1 when ten agents coordinate. Every message, validation step, or context handoff adds latency, token cost, and complexity. At scale, coordination overhead often outweighs the benefit of specialization.

❌ Write conflicts cascade.
If multiple agents write to the same source of truth: a shared database, knowledge base, or document, their changes can collide. One agent updates a schema, another updates logic on top of the old version, and suddenly the system’s state fragments.

TL;DR: Not every workflow needs a multi-agent setup.

When tasks are independent, read-heavy, and parallelizable, multi-agent systems shine. But for sequential logic and tight dependencies, a single-agent pipeline is still faster, cheaper, and easier to maintain.

As always, Galileo has published a new phenomenal free handbook- Mastering Multi-Agent Systems.
 
If you want to go deeper into how these systems work, why they fail, how to evaluate them, explore different architectures, and even build LLMOps around them, this is one of the best reads out there: https://lnkd.in/diptdEC2

#GalileoPartner

Multi-Agent AI System Cheatsheet 

I learned Design Thinking on my first day as a data scientist at IBM, and it quietly became one of the most useful mindsets in my AI career. It taught me to slow down, understand the real problem, and build solutions that actually land with users.

Here’s how I still use it today:

→ Solve the right problem
Most AI teams jump into modeling too early. Design Thinking forces you to reframe, question assumptions, and check if the problem is even defined correctly.  

→ Diverge before you converge
Explore many possibilities, then narrow down. This prevents tunnel vision and leads to better architectures, better experiments, and better product decisions

→ Use the 2x2 grid
Am I exploring or deciding?
Am I thinking about the problem or the solution?
This simple check saves me from going down the wrong path.  

→ Observe behavior, not opinions
The real insights come from watching users, not asking them. The same applies when evaluating agents, prototypes, or internal tools.  

→ Prototype fast, assign ownership
Ideas die when nobody owns them. Execution is where Design Thinking turns into impact.  

This mindset shaped how I build AI systems then, and it still shapes how I build them now.

♻️Share this with your network

Design Thinking Framework

Last week I came home to a little surprise from OpenAI filled with their new books, 100 Chats for Studying, Career, and College Life. 

I flipped through them right away and loved how visual and genuinely interesting they’ve made each section. It feels approachable, practical, and fun in a way most tech books don’t even try for.

If you want to explore the prompts yourself, you can check them out at this link: https://lnkd.in/ddycaDAg

It’s a pretty cool resource to keep on your desk as you build, study, or just experiment with new ideas.

Here is my absolute fav one which I wish I could have used as a student 👇  
I want to understand [concept]. Explain it with an analogy to real-world items.

Let me know in the comments below which ones is your favorite?

If you’re getting started as an AI engineer, this one’s for you 👇

Here are 12 things you need to learn if you want to become a real AI engineer (or level up as one):

1️⃣ LoRA & qLoRA fine-tuning
Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check.
https://lnkd.in/dxAfNJjP
https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF)
Understand how to align model behavior with human expectations using curated reward signals.
https://lnkd.in/da4qf5JV
https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR)
Use simulated or programmatic reward models when human feedback is too expensive.
https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals
Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs.
https://lnkd.in/dFkgD6RQ
https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures
Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases.
https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases
Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs.
https://lnkd.in/d8zeWbmn
https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought
Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context.
https://lnkd.in/djbJD578
https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration
Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs.
https://lnkd.in/dfjvxhvP
https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies
Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures.
https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation
Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference.
https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring
Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues.
https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking
Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale.
https://lnkd.in/dP_NJXg9

〰️〰️〰️〰️
♻️ Share this with your network 
🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re getting started with building AI agents, this Agentic AI Toolkit Cheatsheet will come in handy 👇

You’ll quickly realize it’s not just about choosing a model. You need orchestration, memory, retrieval, evaluation, and a solid deployment stack.

And if you’re not careful, your architecture turns into a mess of disconnected tools.

That’s why I put together this Agentic AI Toolkit Cheatsheet.

It organizes the full stack of what goes into building production-grade AI agents- from model hosting to feedback loops, so you can stop guessing what’s missing in your pipeline and start shipping actual systems.

Here’s what it covers:

🔧 Foundation Models & Hosting
🎛️ Model Customization (SFT, RFT, RLHF)
🤖 Agent Frameworks
🧩 Orchestration & Workflow
📥 Data Ingestion & Preprocessing
🧠 Embedding & Retrieval
🧷 Memory & Knowledge Storage
🔌 Tool Integration
🧪 Evaluation & Monitoring
🖥️ User Interfaces
🚀 Deployment & Hosting


💡 A few tips if you’re working on your first agentic stack:

→ Choose one tool per layer to start, depth over breadth.
→ Build iteratively and introduce evaluation as early as possible.
→ Use event-driven workflows to manage complexity.
→ Think about memory and feedback as first-class components, not afterthoughts.


📌 Bookmark this.
📚 Use it to audit your stack.
🛠️ Build with it.
📤 Share it with a teammate who’s lost in the sea of GenAI tooling.

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This Week of AI is here⚡️
Another packed week in the world of AI- here’s your quick TL;DR of everything that happened across the AI ecosystem this week 👇

- Apple Forges Billion-Dollar AI Alliance with Google for Siri Overhaul
- Anthropic Foils First Documented Large-Scale Autonomous AI Cyberattack
- World Labs Unveils Marble, A Multimodal World Model for Spatial Intelligence
- OpenAI Rolls Out GPT-5.1 with Adaptive Reasoning and Dual Personalities
- Microsoft Creates MAI Superintelligence Team Led by Mustafa Suleyman
- Google Launches ‘Agentic Checkout’ and AI Shopping Tools for Holiday Season
- IBM Joins OpenSearch Software Foundation as Premier Member

♻️ Share this with your network
🔔 Follow me (Aishwarya Srinivasan) for no BS data and AI updates and insights

This week, the AI landscape was defined by blockbuster partnerships, a major infrastructure upgrade, and a chilling preview of autonomous cyber warfar

Open article: This Week in AI: Week of 16th November 2025 by Aishwarya Srinivasan • 3 min read

This Week in AI: Week of 16th November 2025

Thanksgiving week, yet a lot happening in this week in AI!
Catch everything you need to know here 👇

Vision-Language Models connect what AI sees with what it reads and reasons.

They’re the foundation of AI systems that can interpret charts, medical images, retail shelves, or product catalogs.

But a generic VLM doesn’t understand your domain’s visual language.
That’s where fine-tuning becomes essential.

𝐖𝐡𝐲 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐚 𝐕𝐋𝐌 𝐦𝐚𝐭𝐭𝐞𝐫𝐬

A pretrained VLM already knows the basics of visual-text reasoning.
Fine-tuning helps it specialize for your domain.

→ In healthcare, it learns to detect anomalies in MRIs and X-rays.
→ In retail, it interprets shelf images and product layouts.
→ In enterprise, it extracts structured data from invoices and reports.

You’re not rebuilding intelligence, you’re refining perception to fit your use case.

𝐇𝐨𝐰 𝐋𝐨𝐑𝐀 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭

Full model fine-tuning is expensive and compute-heavy.
Low-Rank Adaptation (LoRA) keeps the base model frozen and trains only small adapter layers.

That means:
→ Faster training cycles
→ Smaller memory footprint
→ Lower compute costs
→ Domain adapters that are easy to swap in and out

You can maintain one base model and multiple lightweight adapters for each use case such as invoices, medical forms, or retail analytics.

𝐈𝐧𝐬𝐢𝐝𝐞 𝐚 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥

A VLM has three main components:
→ 𝐕𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐜𝐨𝐝𝐞𝐫 converts pixels into visual tokens.
→ 𝐅𝐮𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 combines visual and text context for reasoning.
→ 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐃𝐞𝐜𝐨𝐝𝐞𝐫 generates captions, summaries, or structured responses.

Each layer introduces potential failure modes like poor resolution, misaligned regions, or verbose hallucinations. Fine-tuning improves alignment and reliability across these components.

𝐓𝐡𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞

→ 𝐃𝐚𝐭𝐚 𝐝𝐞𝐬𝐢𝐠𝐧: Collect diverse, high-quality, clearly labeled visuals.
→ 𝐓𝐚𝐬𝐤 𝐝𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 : Choose the right setup: captioning, VQA, extraction, or localization.
→ 𝐋𝐨𝐑𝐀 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Train adapters for each domain efficiently.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Use both quantitative metrics and human review for grounding and accuracy.

Always evaluate across different slices such as document type, lighting, and template to surface hidden biases.

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲

Each domain adapter should have its own dataset lineage, version, and evaluation score.
Reliability requires attention to fairness, privacy, consistency, and uncertainty handling.
Fine-tuning doesn’t just improve accuracy, it strengthens governance and ethical alignment.

LoRA fine-tuning makes VLMs faster to adapt, cheaper to deploy, and more aligned with your real-world data. 

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

OSTTRA

- Driving enterprise cloud strategy by designing scalable, secure, and high-performance cloud architectures tailored for business growth.

- Spearheading DevOps & automation initiatives, implementing robust CI/CD pipelines and infrastructure as code (IaC) to enhance deployment efficiency and system reliability.

- Leading DevSecOps transformations, integrating security best practices into the development lifecycle to ensure compliance, mitigate risks, and strengthen cloud security postures.

- Optimizing cloud cost & performance, leveraging cloud-native solutions and FinOps strategies to improve resource utilization and operational efficiency.

- Championing innovation by mentoring teams, fostering a culture of automation, and driving adoption of cutting-edge cloud technologies.

- Collaborating with cross-functional teams to align technology with business goals, ensuring seamless cloud migration and modernization efforts.

- Enforcing governance and compliance, implementing policies and frameworks to maintain security, reliability, and regulatory standards.

Cloud, DevOps & DevSecOps Practice Lead

Publicis Sapient

- Led a global team of architects driving technology incubation, automation, and L3 escalations for Cloud & DevOps.

- Managed CxO-level relationships while coordinating across multiple stakeholders to align cloud strategy with business goals.

- Spearheaded 160+ successful releases, 20+ global training sessions, 30+ technical blogs, and 15+ InfoSec reviews to drive cloud adoption and innovation.

- Standardized CI/CD pipelines using Jenkins – Pipeline as Code, enabling faster and more reliable deployments across multiple solutions.

- Developed and deployed reusable Terraform modules, streamlining infrastructure provisioning and management.

- Designed and implemented a Container Bakery Solution, ensuring secure and standardized container image creation with automated vulnerability scanning.

- Established standardized Kubernetes & Helm templates, enhancing scalability and consistency across cloud-native applications.

- Provided senior leadership, fostering a high-performance team culture, driving innovation, and leading DevOps transformations at an enterprise level.

Senior Manager - Cloud, DevOps & DevSecOps

SapientRazorfish

- Designed & implemented Terraform-based IaC modules to enable seamless infrastructure provisioning across multiple solutions.

- Developed a container bakery solution, standardizing image tag naming conventions and integrating vulnerability scans to enhance container security.

- Pioneered Kubernetes & Helm adoption, creating standardized templates for consistent, scalable deployments.

- Led global delivery teams, driving innovation, automation, and best practices across Cloud & DevOps initiatives.

Senior Specialist - Cloud & DevOps

- Built and scaled DevOps frameworks, implementing CI/CD pipelines, infrastructure automation, and cloud-native solutions for enterprise applications.

- Optimized cloud cost and performance, improving infrastructure efficiency while maintaining security and compliance.

- Fostered a culture of innovation, mentoring teams and driving the adoption of modern DevOps methodologies.

Manager - Cloud & DevOps

- Developed automated build & release pipelines, improving deployment speed and reducing manual efforts.

- Enhanced software delivery processes, integrating cloud and DevOps best practices to streamline workflows.

Senior Associate

Cadence Design Systems

- Led end-to-end build & release automation for semiconductor products, enhancing software delivery efficiency.

- Streamlined project execution by implementing structured tracking and reporting systems for leadership.

- Optimized CI/CD processes to improve deployment speed, reduce failures, and increase overall productivity.

- Collaborated with cross-functional teams, ensuring seamless integration of Cloud & DevOps methodologies.

Senior Member of Technical Staff (SMTS)

One97 Communications Pvt. Ltd.

- Automated build & release pipelines, reducing manual efforts and deployment risks.

- Owned release planning & production deployments, ensuring smooth software rollouts.

- Enhanced version control & configuration management, improving system stability and scalability.

Sr Software Engineer (Configuration, Change & Release Management)

HCL Technologies

- Provided end-to-end technical support for build, release, and software configuration management.

- Defined and implemented optimal branching, merging, and tagging strategies for seamless version control.

- Set up Subversion & Bugzilla on Linux, improving source control and issue tracking efficiency.

- Automated Java-based software builds using Ant, enhancing development workflow and reducing errors.

- Managed deployment across multiple environments (Dev, UAT, Pre-Prod, Prod) via Hudson plugins and custom utilities.

- Developed an advanced automated build system, significantly reducing build time and improving reliability.

- Enhanced code quality with Cobertura & Jacoco for code coverage analysis and Sonar for static code review.

- Troubleshot and resolved production issues, ensuring high availability and smooth releases.

Lead Engineer (Configuration, Change and Release Management)

SAIC

1. Provide technical support and solutions to engineering team in various areas of build, release and software configuration management.
2. Setting up CVS on Windows platform.
3. Automating build for .Net based project using NAnt and Batch Files
4. Deploying build on Dev, Test, Stage and Prod environments on windows platform.
5. Preparing release notes for production releases.
6. Maintained automated build system, debugged build failures, and implemented fixes/enhancements. 
7. Developed functional requirements, designed, and implemented a new enhanced and robust automated build system which reliably accommodated fast growing build/release configurations, reduced build time, increased productivity, provided fail-over mechanism and reproducibility.

Specialties: ClearCase, NAnt, Hudson, Batch Files.

Associate Software Engineer

𝐖𝐢𝐭𝐡 𝟐𝟐+ years of industry experience, I help businesses architect, optimize, and secure their cloud infrastructure—ensuring scalability, automation, and cost-efficiency at every step.

As Director of Cloud Engineering at OSTTRA, I lead global teams in Cloud, DevOps, and DevSecOps, driving innovation and transforming IT landscapes for enterprise-scale success.
What I Bring to the Table:

✔ 𝐂𝐥𝐨𝐮𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 & 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 – Crafting resilient, high-performing cloud ecosystems across AWS, Azure, and GCP.

✔ 𝐃𝐞𝐯𝐎𝐩𝐬 & 𝐃𝐞𝐯𝐒𝐞𝐜𝐎𝐩𝐬 𝐌𝐚𝐬𝐭𝐞𝐫𝐲 – Automating workflows, enforcing security, and streamlining CI/CD with Kubernetes, Helm, Terraform & more.

✔ 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 – Scaling high-impact engineering teams, fostering innovation, and ensuring delivery excellence.

✔ 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐂𝐥𝐨𝐮𝐝 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 – Successfully leading multi-million-dollar cloud transformations with agility and precision.

✔ 𝐂𝐱𝐎-𝐋𝐞𝐯𝐞𝐥 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – Aligning technology with business goals, driving ROI, and shaping digital-first strategies.

Beyond my role, I am a DZone contributor, mentor, and passionate advocate for continuous learning and knowledge sharing.

Let’s connect if you’re looking to discuss cloud transformation, automation, or IT strategy—or if you just love talking tech!

Jaswindder Kummar

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐀𝐖𝐒? 𝐇𝐞𝐫𝐞’𝐬 𝐘𝐨𝐮𝐫 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩  
AWS (Amazon Web Services) is the backbone of modern cloud infrastructure. Whether you’re aiming to become a DevOps engineer or simply looking to broaden your cloud knowledge, AWS is the key to unlocking opportunities in the cloud space. 

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐧𝐚𝐯𝐢𝐠𝐚𝐭𝐞 𝐲𝐨𝐮𝐫 𝐀𝐖𝐒 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐣𝐨𝐮𝐫𝐧𝐞𝐲:

𝟏. 𝐀𝐖𝐒 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬
* AWS Billing Basics: Understand the pricing structure, how to manage costs effectively.
* What is AWS?: Learn about AWS, its services, and how they transform businesses globally.
* Global Infrastructure: Know AWS's global presence and data centers.

𝟐. 𝐂𝐨𝐫𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐞, 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 & 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠
* Compute Services: Explore EC2, Lambda, ECS, EKS, and how AWS powers scalable applications.
* Storage Services: Learn about S3, EBS, EFS, Glacier, and how AWS handles data storage.
* Networking Services: Dive into VPC, ELB, and Route 53 for managing cloud networks.

𝟑. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬
* Relational Databases: MySQL, RDS, and Aurora—understand how AWS handles structured data.
* NoSQL: DynamoDB and other AWS NoSQL options for high-performance, scalable databases.
* In-Memory: How AWS services like ElastiCache improve application performance with in-memory databases.

𝟒. 𝐃𝐞𝐯𝐎𝐩𝐬, 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧
* DevOps Tools: Learn about Infrastructure as Code, CI/CD pipelines, and automation on AWS.
* Monitoring Tools: Gain insight into AWS tools for monitoring performance and managing costs.

𝟓. 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐚𝐭𝐡𝐬
* Certifications: Start with Cloud Practitioner and work your way up to DevOps Engineer or SysOps Engineer.
* AWS Learning Resources: Use AWS Skill Builder, AWS documentation, and other resources for structured learning.

𝟔. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲, 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 & 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞
* IAM (Identity and Access Management): Master users, groups, and roles to ensure secure access.
* Security Tools: Dive into KMS, S3 SSSE, and how AWS handles data encryption.
* Compliance: Understand compliance standards like HIPAA, GDPR, and SOC.

#𝐀𝐫𝐞 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝𝐲 𝐭𝐨 𝐝𝐢𝐯𝐞 𝐝𝐞𝐞𝐩 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝 𝐨𝐟 𝐀𝐖𝐒 𝐚𝐧𝐝 𝐤𝐢𝐜𝐤𝐬𝐭𝐚𝐫𝐭 𝐲𝐨𝐮𝐫 𝐜𝐥𝐨𝐮𝐝 𝐣𝐨𝐮𝐫𝐧𝐞𝐲?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#AWS #Cloud #AWSLearning #DevOps

𝟕𝟓 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐈𝐝𝐞𝐚𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬

We often hear about AI agents in theory - autonomous systems that can think, decide, and act.
𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐥𝐨𝐨𝐤 𝐥𝐢𝐤𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝?

👉 𝐓𝐡𝐢𝐬 𝐯𝐢𝐬𝐮𝐚𝐥 𝐜𝐚𝐩𝐭𝐮𝐫𝐞𝐬 𝟕𝟓 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐚𝐠𝐞𝐧𝐭 𝐢𝐝𝐞𝐚𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐝𝐨𝐦𝐚𝐢𝐧𝐬 𝐥𝐢𝐤𝐞:

* Productivity & Admin – Meeting Insights, Document Summarizer
* DevOps & Cloud – Bug Triage, Auto-Scaler, Cost Optimizer
* Cybersecurity & IT – Threat Hunting, Patch Updates, Access Control
* Sales & Marketing – Lead Scoring, Content Ideation, SEO Optimization
* Finance & HR – Expense Analysis, Resume Screening, Employee Sentiment
* AI & Analytics – Bias Detection, Model Deployment, Anomaly Detection

The potential is massive - these agents are not just futuristic concepts; they are building blocks of next-gen workflows, automation, and decision-making.

💡 𝐓𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 “𝐂𝐚𝐧 𝐀𝐈 𝐝𝐨 𝐭𝐡𝐢𝐬?” 𝐛𝐮𝐭 “𝐖𝐡𝐢𝐜𝐡 𝐚𝐠𝐞𝐧𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐟𝐢𝐫𝐬𝐭?”

👉 𝐈𝐟 𝐲𝐨𝐮 𝐡𝐚𝐝 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐨𝐧𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐭𝐨𝐝𝐚𝐲 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤/𝐥𝐢𝐟𝐞 𝐞𝐚𝐬𝐢𝐞𝐫-
𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐰𝐨𝐮𝐥𝐝 𝐢𝐭 𝐛𝐞?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#AI #AIagents #Automation #FutureOfWork #cloud #devops #security

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐢𝐬 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥… 𝐛𝐮𝐭 𝐨𝐧𝐥𝐲 𝐢𝐟 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐰𝐡𝐢𝐜𝐡 𝐭𝐨𝐨𝐥𝐬 𝐭𝐨 𝐮𝐬𝐞.

Think of Kubernetes as the brain that runs and manages your containers.
But just like a brain, it needs eyes, ears, arms, and a security system that is where these tools come in.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧:

1. Monitoring & Observability: Tools like Grafana, Prometheus, and Datadog keep an eye on your cluster health and performance so you spot issues before they hit production.

2. Infrastructure Orchestration: Terraform, Pulumi, and Argo help you automate and scale your infrastructure without breaking a sweat.

3. Networking: Istio, Linkerd, and Traefik ensure your services talk to each other smoothly and securely.

4. Cluster Management: Minikube, Rancher, and OpenShift help you create, manage, and upgrade Kubernetes clusters without the headache.

5. Security: Tools like Falco, Trivy, and Kube-Bench scan for vulnerabilities, misconfigurations, and suspicious behavior.

6. Container Runtime: Docker, containerd, and Firecracker are what actually run your containers behind the scenes.

𝐈𝐧 𝐬𝐡𝐨𝐫𝐭: 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐞𝐧𝐠𝐢𝐧𝐞.
These tools are the full pit crew keeping it running at top speed.

𝐈𝐟 𝐲𝐨𝐮 𝐡𝐚𝐝 𝐭𝐨 𝐦𝐚𝐬𝐭𝐞𝐫 𝐣𝐮𝐬𝐭 𝐨𝐧𝐞 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐨𝐟 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐭𝐨𝐨𝐥𝐬 𝐭𝐡𝐢𝐬 𝐲𝐞𝐚𝐫, 𝐰𝐡𝐢𝐜𝐡 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐜𝐡𝐨𝐨𝐬𝐞?

𝐖𝐡𝐚𝐭 𝐌𝐚𝐤𝐞𝐬 𝐊𝐚𝐟𝐤𝐚 𝐒𝐨 𝐅𝐚𝐬𝐭? 𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭!
If you have worked with real-time data streaming, you must have heard about Kafka. But have you ever wondered why it is so fast in processing massive amounts of data? 

𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐢𝐭 𝐝𝐨𝐰𝐧:

𝟏. 𝐙𝐞𝐫𝐨 𝐂𝐨𝐩𝐲 𝐃𝐚𝐭𝐚 𝐅𝐥𝐨𝐰:  Kafka uses zero-copy technology that allows data to bypass multiple layers of memory copy operations. This is crucial for high-speed data transfer.

𝟐. 𝐑𝐀𝐌 𝐖𝐫𝐢𝐭𝐞: When a producer writes data, Kafka directly writes to the RAM, ensuring ultra-fast data storage before moving to buffers.

𝟑. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐌𝐨𝐯𝐞𝐦𝐞𝐧𝐭: Kafka minimizes disk writes by copying data from RAM to OS buffers, then to the Socket Buffer, and finally to the NIC Buffer, keeping the system in constant motion.

𝟒. 𝐅𝐚𝐬𝐭 𝐂𝐨𝐧𝐬𝐮𝐦𝐞𝐫 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲: Once the data reaches the NIC Buffer, it is immediately sent to the consumer, ensuring real-time consumption without unnecessary delays.

𝟓. 𝐏𝐞𝐫𝐢𝐨𝐝𝐢𝐜 𝐃𝐢𝐬𝐤 𝐒𝐲𝐧𝐜𝐢𝐧𝐠:  Kafka also syncs data to disk periodically, but this process doesn’t block the primary data flow. It runs in parallel, ensuring high throughput.

𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫?
* Reduced Latency: Kafka’s architecture ensures that every component communicates with minimal delay, allowing it to handle millions of events per second with low latency.

* Scalability: Kafka can efficiently scale to meet high throughput requirements due to this efficient data transfer mechanism.

If you are working on a high-volume, real-time data pipeline, Kafka is a powerful tool that can significantly speed up your workflows.

---

Have you leveraged Kafka’s architecture in your systems? How did it help reduce latency and increase throughput?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#Kafka #CloudComputing #DevOps

𝐓𝐡𝐞 𝐔𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐟𝐨𝐫 𝐂𝐥𝐨𝐮𝐝 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫𝐬 
𝐖𝐡𝐢𝐜𝐡 𝐎𝐧𝐞 𝐒𝐡𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐂𝐡𝐨𝐨𝐬𝐞?

𝐀𝐫𝐞 𝐲𝐨𝐮 𝐧𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐨𝐟 𝐜𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐜𝐥𝐨𝐮𝐝 𝐥𝐨𝐚𝐝 𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐫 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧?

With several options available across cloud providers, it can be overwhelming. AWS, Azure, and Google Cloud all offer different load balancing solutions depending on your needs. 

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐠𝐮𝐢𝐝𝐞 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐭𝐡𝐢𝐬 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐞𝐚𝐬𝐢𝐞𝐫:

𝟏. 𝐆𝐥𝐨𝐛𝐚𝐥 𝐯𝐬. 𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐓𝐫𝐚𝐟𝐟𝐢𝐜:
* For global traffic, you can use AWS Global Accelerator or Azure Front Door.
* For regional traffic, Network Load Balancer (AWS), Azure Load Balancer, or Google Cloud’s Network Load Balancer works best.

𝟐. 𝐖𝐞𝐛 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 (𝐇𝐓𝐓𝐏/𝐇𝐓𝐓𝐏𝐒):
* When you need SSL offload for Web Applications, AWS, Azure, and Google Cloud all offer Application Load Balancers tailored for HTTP/HTTPS.

𝟑. 𝐇𝐨𝐬𝐭𝐢𝐧𝐠 𝐎𝐩𝐭𝐢𝐨𝐧𝐬:
* Depending on your hosting, PaaS services like AWS Elastic Beanstalk or Azure App Services can be paired with the appropriate load balancers.

𝟒. 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭:
* For internal TCP/UDP load balancing, Azure's Azure Load Balancer or Google Cloud's TCP/UDP Load Balancing is your go-to option.

𝟓. 𝐒𝐒𝐋 𝐎𝐟𝐟𝐥𝐨𝐚𝐝:
* If SSL termination is needed, AWS Global Accelerator and Azure Front Door both manage this efficiently.

By understanding these choices, you’ll know which load balancer is best suited to your application’s needs. Make sure you pick the right one to optimize traffic management and performance.

Have you already picked your cloud load balancer? What factors do you consider most important when making the decision?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#CloudComputing #DevOps #AWS #Azure #GoogleCloud

Over the last two decades in DevSecOps and cloud transformation, I’ve seen many shifts, virtualization, containers, serverless, zero trust.

But AI agents are a completely different kind of change.

They don’t just generate content.
They act, they plan, and they have real permissions in our systems.
That makes their security challenges unique.

Google’s new paper on Secure AI Agents makes one thing clear:

✅ 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐜𝐫𝐞𝐚𝐭𝐞 𝐧𝐞𝐰 𝐫𝐢𝐬𝐤𝐬 𝐰𝐞’𝐯𝐞 𝐧𝐞𝐯𝐞𝐫 𝐡𝐚𝐝 𝐭𝐨 𝐦𝐚𝐧𝐚𝐠𝐞 𝐛𝐞𝐟𝐨𝐫𝐞.

The two biggest risks:

1️⃣ Rogue Actions:
Agents can misinterpret tasks or be tricked by hidden instructions, causing unwanted emails, data exposure, or system changes.

2️⃣ Sensitive Data Leaks:
Attackers can guide an agent to reveal private data through URLs, markdown, code, or insecure output.

✅ 𝐇𝐨𝐰 𝐭𝐨 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐒𝐢𝐦𝐩𝐥𝐞 𝐕𝐞𝐫𝐬𝐢𝐨𝐧)

1. Human-in-Control:
Agents must know who the real user is.
High-risk actions must always ask for confirmation.

2. Least Privilege (Dynamic):
Give the agent only the permissions needed for the task, nothing more.

3. Full Visibility:
Teams must see what the agent did, what data it used, and what it planned.
No visibility = no security.

✅ 𝐓𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: 𝐇𝐲𝐛𝐫𝐢𝐝 𝐃𝐞𝐟𝐞𝐧𝐬𝐞-𝐢𝐧-𝐃𝐞𝐩𝐭𝐡

Google recommends a mix of:

* Hard rules (policy engines, limits)
* AI-based defenses (guard models, safety training)

Both together create safer agent behavior.

AI agents are powerful, but without guardrails, they become risky.
To use them safely, we need clear human control, limited permissions, and strong visibility from day one.

Google’s Approach for Secure AI Agents

The more I study the DevOps & Cloud 2025 skills landscape, the clearer it becomes:

we’re no longer working in isolated job roles... we’re operating in a shared engineering ecosystem.

The matrix in the image says it all.

For years, we treated Cloud, DevOps, SRE, Platform, and DevSecOps as distinct lanes:

* Cloud Engineers focused on provisioning and IAM
* DevOps owned CI/CD and automation
* SREs obsessed over latency, toil, and reliability
* Platform Engineers built internal tooling
* Security teams worked as gatekeepers on the side

But 2025 is different.

The technical stack has converged.
Terraform, Kubernetes, observability stacks, pipelines, threat modeling... they’ve become the common language across all these roles.

Not equal depth, but equal exposure.

You may be hired as a Cloud Engineer, but the reality is:

* You will write IaC
* You will containerize workloads
* You will debug pipeline failures
* You will build dashboards
* You will think about least-privilege and policy

The market now rewards engineers who can move horizontally... people who understand how their piece fits into the larger system.

Specialization still matters.
Depth still matters.
But rigidity doesn’t.

The future belongs to engineers who are deep in one pillar and fluent across the rest.

To help with that, I’m starting a new newsletter series where I’ll break down each role in this matrix:

* What “high-level skill” actually means in practice
* Where the depth truly differentiates you
* And the roadmap to transition into any of these paths without drowning in tools

If you want that kind of clarity for your own career, stay tuned.

𝐌𝐨𝐬𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐛𝐮𝐢𝐥𝐭 𝐭𝐨 𝐬𝐜𝐚𝐥𝐞. 𝐕𝐞𝐫𝐲 𝐟𝐞𝐰 𝐚𝐫𝐞 𝐛𝐮𝐢𝐥𝐭 𝐭𝐨 𝐬𝐭𝐚𝐲 𝐬𝐞𝐜𝐮𝐫𝐞 𝐰𝐡𝐢𝐥𝐞 𝐬𝐜𝐚𝐥𝐢𝐧𝐠.  

Here’s the ultimate cheatsheet every developer and architect should keep handy when designing secure systems covering 12 key layers of protection from day one.

𝟏. 𝐃𝐢𝐬𝐚𝐬𝐭𝐞𝐫 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲
Always plan for failure. Have a Disaster Recovery plan, backup redundancy, and the ability to recover from random attacks or data center outages.

𝟐. 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Your first defense is identity. Use strong password policies and enforce Multi-Factor Authentication (MFA) for all logins.

𝟑. 𝐀𝐮𝐭𝐡𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Limit access with the least privilege principle. Implement role-based access control and conduct regular access reviews.

𝟒. 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐢𝐨𝐧
Protect sensitive data in transit and at rest using TLS and proper key management practices.

𝟓. 𝐕𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
Continuously scan, patch, and monitor your systems to close potential security gaps before attackers find them.

𝟔. 𝐀𝐮𝐝𝐢𝐭 & 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞
Follow compliance standards like GDPR and HIPAA. Maintain audit logs and regular system checks.

𝟕. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲
Segment your network, use firewalls, intrusion detection systems, and secure DNS to prevent lateral movement.

𝟖. 𝐓𝐞𝐫𝐦𝐢𝐧𝐚𝐥 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲
Encrypt hard drives, manage endpoints, and secure laptops or POS systems that connect to your infrastructure.

𝟗. 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐲 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞

Always expect the unexpected. Create incident response plans and conduct regular security drills.

𝟏𝟎. 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲
Use trusted base images, scan containers for vulnerabilities, and secure container runtimes and clusters.

𝟏𝟏. 𝐀𝐏𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲
Secure APIs are non-negotiable. Use OAuth2, rate limiting, and input validation to prevent misuse and DDoS attacks.

𝟏𝟐. 𝐓𝐡𝐢𝐫𝐝-𝐏𝐚𝐫𝐭𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
Your security is only as strong as your weakest vendor. Conduct risk assessments, monitor access, and secure integrations.

A truly secure system is not built overnight it is designed layer by layer.

Which of these 12 layers do you think teams underestimate the most?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#CyberSecurity #SystemDesign #CloudSecurity #DevSecOps

How Netflix Scaled DevOps Culture To Power 𝟐𝟕𝟎 𝐌𝐢𝐥𝐥𝐢𝐨𝐧 𝐔𝐬𝐞𝐫𝐬

When you think of Netflix, you think of shows, not servers.
But behind every “Play Next Episode” button... there’s one of the most advanced DevOps cultures in the world.

Let’s break down how Netflix turned DevOps from a practice into a superpower.

𝟏. 𝐓𝐡𝐞 𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐏𝐨𝐢𝐧𝐭:
In 2008, Netflix faced a massive database corruption that took its DVD rental service offline for days.
That’s when the leadership made a bold decision, move everything to the cloud.
Not partially. Not hybrid. Fully cloud-native.
Amazon Web Services (AWS) became their foundation.

𝟐. 𝐓𝐡𝐞 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐡𝐢𝐥𝐨𝐬𝐨𝐩𝐡𝐲:
Netflix didn’t just adopt DevOps tools, they embraced a mindset.

Their core belief:

> “𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐨𝐰𝐧 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐛𝐮𝐢𝐥𝐝: 𝐟𝐫𝐨𝐦 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐭𝐨 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬.”

Every developer at Netflix is empowered to deploy code to production.
No middle layer. No waiting on Ops.
This ownership created a fast feedback loop and accountability at scale.

𝟑. 𝐂𝐡𝐚𝐨𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 – 𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐅𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐈𝐧𝐭𝐨 𝐋𝐞𝐬𝐬𝐨𝐧𝐬:
Netflix knew the only way to build resilience was to test failure regularly.

So they created Chaos Monkey... a tool that randomly shuts down servers in production.
Sounds risky? It was revolutionary.
It taught teams to design systems that could recover automatically and stay reliable — even when things go wrong.

This became the foundation of their “𝐒𝐢𝐦𝐢𝐚𝐧 𝐀𝐫𝐦𝐲”, a suite of resilience tools used across the industry today.

𝟒. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲, 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭:
Netflix deploys hundreds of updates every day.
They built Spinnaker, an open-source CD tool, to automate and monitor deployments globally.

Developers can push features faster, test quickly, and roll back safely — all while serving millions of concurrent users.

𝟓. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬:
Every service at Netflix is deeply monitored.
Metrics, logs, traces — all feed into systems like Atlas and Hystrix, enabling predictive alerts and auto-scaling decisions.
Instead of waiting for an outage, the system anticipates one.

𝟔. 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭:

* Zero downtime during massive traffic spikes (like new series launches).
* Faster innovation cycles — from idea to release in hours, not weeks.
* A culture where developers and operations work as one team.


If you’re a cloud or DevOps engineer, take a note from Netflix’s story:
𝐃𝐞𝐯𝐎𝐩𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬... it’s about empowering people to move fast and stay safe.


♻️ Repost this to help your network get started 
➕ Follow Jaswindder for more

Netflix Case Study

𝐘𝐨𝐮𝐫 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐚𝐬 𝐂𝐨𝐝𝐞 (𝐈𝐚𝐂) 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐛𝐥𝐢𝐧𝐝 𝐬𝐩𝐨𝐭 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐜𝐥𝐨𝐮𝐝 𝐬𝐞𝐭𝐮𝐩  
IaC tools like Terraform, Ansible, and Chef make infrastructure deployment fast and consistent but one small misconfiguration in a CI/CD pipeline can expose your entire cloud environment.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐫𝐞𝐚𝐥𝐥𝐲 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐈𝐚𝐂 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐠𝐨𝐞𝐬 𝐰𝐫𝐨𝐧𝐠 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐭𝐨 𝐩𝐫𝐞𝐯𝐞𝐧𝐭 𝐢𝐭:

𝟏. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐈𝐚𝐂
Instead of manually configuring servers, you define your infrastructure using code (Terraform, Ansible, etc.). The tool then turns that code into actual infrastructure.

𝟐. 𝐖𝐡𝐲 𝐈𝐚𝐂
IaC brings speed, consistency, and auditability. You can track every change, deploy faster, and reduce human error. But when used carelessly, it also automates vulnerabilities.

𝟑. 𝐓𝐡𝐞 𝐑𝐢𝐬𝐤 𝐙𝐨𝐧𝐞
IaC interacts deeply with CI/CD, cloud storage, and Git workflows. If your IaC scripts or pipelines are misconfigured, attackers can exploit them to steal secrets or modify builds.

𝟒. 𝐓𝐡𝐞 𝐀𝐭𝐭𝐚𝐜𝐤 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨
An attacker gains access to a developer’s workstation and modifies the cloud build configuration to pull secrets from your Secret Manager.
Once committed and approved in Git, the pipeline executes the malicious configuration leaking your cloud credentials.

𝟓. 𝐓𝐡𝐞 𝐑𝐞𝐬𝐜𝐮𝐞
Secure pipelines detect misconfigurations early. Proper validation, pre-commit checks, and automated policy enforcement (like Sentinel or Open Policy Agent) stop the pipeline before damage is done.

The lesson: IaC isn’t insecure unguarded pipelines are.

Do you validate every IaC change before it hits your production environment?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#DevSecOps #InfrastructureAsCode #CloudSecurity #TerraformSecurity

𝐘𝐨𝐮𝐫 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐥𝐞𝐚𝐤𝐢𝐧𝐠 𝐬𝐞𝐜𝐫𝐞𝐭𝐬 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰 𝐚𝐧𝐝 𝐲𝐨𝐮 𝐰𝐨𝐮𝐥𝐝𝐧’𝐭 𝐞𝐯𝐞𝐧 𝐤𝐧𝐨𝐰 𝐢𝐭 
Most developers focus on automation speed, but forget the silent threat hiding inside every build: unsecured credentials.

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐭𝐨 𝐬𝐞𝐜𝐮𝐫𝐞 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭’𝐬 𝐭𝐨𝐨 𝐥𝐚𝐭𝐞:

𝟏. 𝐂𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥 𝐒𝐭𝐨𝐫𝐚𝐠𝐞
Never store plaintext credentials in Jenkins files. Use Jenkins Credentials Plugin to securely store and retrieve encrypted data.

𝟐. 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬
Hardcoding credentials in scripts is a disaster waiting to happen. Always pass them securely through environment variables and restrict visibility in logs.

𝟑. 𝐒𝐞𝐜𝐫𝐞𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
Use tools like HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault to manage your secrets centrally. Stop embedding keys in pipelines.

𝟒. 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐞𝐝 𝐂𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐬
Even if someone gains access to your CI/CD server, encrypted credentials make it harder to exploit data. Configure automatic encryption in your CI/CD tools.

𝟓. 𝐋𝐞𝐚𝐬𝐭 𝐏𝐫𝐢𝐯𝐢𝐥𝐞𝐠𝐞
Only give users the permissions they absolutely need. Restrict access to sensitive configurations and credentials.

𝟔. 𝐌𝐮𝐥𝐭𝐢-𝐅𝐚𝐜𝐭𝐨𝐫 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Add an extra layer of security with MFA for Jenkins and other connected systems. It’s a simple yet powerful step.

𝟕. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐀𝐮𝐝𝐢𝐭𝐢𝐧𝐠
Regularly audit logs and monitor unusual credential usage. Prevention is better than detection.

𝟖. 𝐒𝐞𝐫𝐯𝐞𝐫 𝐈𝐬𝐨𝐥𝐚𝐭𝐢𝐨𝐧
Keep Jenkins and build servers behind firewalls or VPNs to limit access and reduce risks of data theft.

𝟗. 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐂𝐨𝐝𝐞
Use version-controlled Jenkinsfiles. Manual scripts often introduce misconfigurations that leak secrets.

𝟏𝟎. 𝐀𝐜𝐜𝐞𝐬𝐬 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
Implement role-based access control (RBAC) to properly segregate credentials and jobs.

𝟏𝟏. 𝐓𝐞𝐚𝐦 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝟏𝟏. 𝐓𝐞𝐚𝐦 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠
Even the best tools fail without awareness. Train your developers on CI/CD security best practices and responsible secret handling.

Security is not just about tools it is about discipline at every stage of automation.

Which one of these CI/CD practices do you think most teams still overlook?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#DevSecOps #CICDSecurity #CloudSecurity

𝐖𝐡𝐲 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 (𝐀𝐈𝐎𝐩𝐬) 𝐯𝐬 𝐃𝐞𝐯𝐎𝐩𝐬 𝐯𝐬 𝐌𝐋𝐎𝐩𝐬 𝐯𝐬 𝐋𝐋𝐌𝐎𝐩𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐍𝐞𝐱𝐭 𝐁𝐢𝐠 𝐒𝐡𝐢𝐟𝐭 𝐢𝐧 𝐓𝐞𝐜𝐡

But which of these evolving frameworks should you focus on?

𝟏. 𝐀𝐈𝐎𝐩𝐬: 
Revolutionizing IT operations with AI. From ingesting telemetry to auto-remediation, AIOps is crucial for detecting anomalies and continuously tuning systems.

𝟐. 𝐌𝐋𝐎𝐩𝐬:  
Bridging the gap between data and machine learning models. It’s about gathering data, versioning it, and ensuring that your models stay relevant with continuous training.

𝟑. 𝐃𝐞𝐯𝐎𝐩𝐬: 
The backbone of efficient software development. With planning, version control, automated testing, and infrastructure as code, DevOps ensures a seamless pipeline from development to deployment.

𝟒. 𝐋𝐋𝐌𝐎𝐩𝐬: 
The emerging frontier for large language models. From use-case scoping to prompt design, LLMOps is setting the stage for AI applications that can handle real-world challenges like toxicity filters and latency SLAs.

As AI, machine learning, and automation converge, professionals in these fields will need to master multiple aspects of these frameworks. The lines between these domains are blurring. The next step is understanding how they integrate and evolve.

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#AI #DevOps #MLOps #LLMOps #AIOps

𝐇𝐨𝐰 𝐭𝐨 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐙𝐞𝐫𝐨 𝐓𝐫𝐮𝐬𝐭 𝐢𝐧 𝐘𝐨𝐮𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐔𝐧𝐛𝐫𝐞𝐚𝐤𝐚𝐛𝐥𝐞 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲

𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐭𝐨 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐢𝐭 𝐢𝐧𝐭𝐨 𝐲𝐨𝐮𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐚𝐧𝐝 𝐞𝐧𝐬𝐮𝐫𝐞 𝐮𝐧𝐛𝐫𝐞𝐚𝐤𝐚𝐛𝐥𝐞 𝐩𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧:

𝟏. 𝐒𝐂𝐀 (𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬):
* Scans containers and registries for security issues in open-source components.
* Identifies vulnerabilities, security flaws, and license issues.
* Tools: Sonatype, Checkmarx

𝟐. 𝐒𝐀𝐒𝐓 (𝐒𝐭𝐚𝐭𝐢𝐜 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠):
* Analyzes code for security risks before running the app.
* Helps catch problems early in the development process, even if the app isn’t running.
* Tools: Semgrep, Checkmarx, Veracode, SonarQube

𝟑. 𝐃𝐀𝐒𝐓 (𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠):
* Tests the app in real-time to simulate real-world threats.
* Finds vulnerabilities by attacking the app’s user interface and APIs.
* Tools: BurpSuite, ZAP, Checkmarx

𝟒. 𝐑𝐀𝐒𝐏 (𝐑𝐮𝐧𝐭𝐢𝐦𝐞 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐥𝐟-𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧):
* Protects the app while it’s running by blocking attacks as they happen.
* Takes action to stop malicious activity during real-time usage.
* Tools: Checkmarx

𝟓. 𝐖𝐀𝐅 (𝐖𝐞𝐛 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐅𝐢𝐫𝐞𝐰𝐚𝐥𝐥):
* Protects web apps by filtering and monitoring incoming traffic.
* Blocks malicious traffic before it can reach the app.
* Tools: Checkmarx

𝟔. 𝐈𝐀𝐒𝐓 (𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠):
* Monitors the running app for security issues during runtime.
* Watches how the app behaves and reacts to threats.
* Tools: Checkmarx

𝟕. 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲:
* Secures containers and detects vulnerabilities in container images and their settings.
* Ensures lightweight, isolated environments remain safe.
* Tools: Clair

𝟖. 𝐈𝐚𝐂 (𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐚𝐬 𝐂𝐨𝐝𝐞):
* Uses code to manage infrastructure (servers, networks).
* Automates security for infrastructure configuration.
* Tools: Checkov, KICS, TFsec

Zero Trust is about never trusting by default and continuously verifying everything. Integrating these tools and practices into your DevOps pipeline is the only way to ensure real-time security and long-term peace of mind.

How are you integrating Zero Trust into your DevOps pipeline?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#ZeroTrust #DevOps #CyberSecurity #InfoSec

𝐓𝐨𝐩 𝟏𝟎 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰
If you are preparing for a Terraform-related interview, here are the top 10 questions you should be ready to answer:

𝟏. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐒𝐭𝐚𝐭𝐞 𝐅𝐢𝐥𝐞?
* The `terraform.tfstate` file is the source of truth for your infrastructure.
* It maps Terraform configuration to real-world resources.
* Tip: Store it remotely (S3, Azure Blob, Terraform Cloud) for team collaboration and state locking.

𝟐. 𝐖𝐡𝐚𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐬 𝐈𝐟 𝐘𝐨𝐮 𝐋𝐨𝐬𝐞 𝐭𝐡𝐞 𝐒𝐭𝐚𝐭𝐞 𝐅𝐢𝐥𝐞?
* Terraform forgets what’s deployed and may try to recreate everything.
* Best Practice: Always enable remote backends and versioning for easy recovery.

𝟑. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐃𝐫𝐢𝐟𝐭?
* Drift occurs when actual infrastructure differs from what’s defined in the code (e.g., manual changes to an EC2 instance in AWS).
* Detect it with: `terraform plan`

𝟒. 𝐇𝐨𝐰 𝐃𝐨 𝐘𝐨𝐮 𝐅𝐢𝐱 𝐃𝐫𝐢𝐟𝐭?
* Update Terraform code to match the actual changes, or run `terraform apply` to restore the desired state.

𝟓. 𝐇𝐨𝐰 𝐭𝐨 𝐇𝐚𝐧𝐝𝐥𝐞 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 (𝐃𝐞𝐯, 𝐒𝐭𝐚𝐠𝐞, 𝐏𝐫𝐨𝐝)?
* Use Workspaces or separate state files/backends for each environment. This keeps your infrastructure isolated and manageable.

𝟔. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐚𝐧𝐝 𝐂𝐥𝐨𝐮𝐝𝐅𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧?
* Terraform is multi-cloud and uses HCL (HashiCorp Configuration Language), while CloudFormation is AWS-only and uses YAML/JSON.

𝟕. 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬?
* Providers are plugins that let Terraform manage resources on specific platforms (AWS, Azure, GCP, etc.).
* They translate Terraform configurations into real API calls.

𝟖. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐌𝐨𝐝𝐮𝐥𝐞?
* A module is a reusable collection of Terraform configurations.
* It helps structure, reuse, and share infrastructure code efficiently.

𝟗. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 `𝐭𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐢𝐧𝐢𝐭`?
* `terraform init` initializes your working directory containing Terraform configuration files, preparing it for deployment.

𝟏𝟎. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 `𝐭𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐩𝐥𝐚𝐧` 𝐚𝐧𝐝 `𝐭𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐚𝐩𝐩𝐥𝐲`?
* `terraform plan` shows what will change (a preview).
* `terraform apply` executes and makes the actual changes.
* Tip: Always review the plan before applying.

Mastering these questions can help you ace your Terraform interviews and strengthen your DevOps knowledge.

Are you ready for your next Terraform interview?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#Terraform #DevOps #CloudInfrastructure #InfrastructureAsCode

Terraform interview

𝐀 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭 𝐨𝐧 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐄𝐯𝐞𝐫𝐲 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰
Messaging patterns are at the core of building scalable and efficient communication between services. 

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐦𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐧𝐝 𝐭𝐨𝐨𝐥𝐬 𝐭𝐨 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐭𝐡𝐞𝐦:

𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬:

𝟏. 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐐𝐮𝐞𝐮𝐞:
* A Producer sends messages to a message queue, which the Consumer processes.
* Use Case: Asynchronous communication where the Producer and Consumer are decoupled.

𝟐. 𝐏𝐮𝐛/𝐒𝐮𝐛 (𝐏𝐮𝐛𝐥𝐢𝐬𝐡/𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞):
* Services subscribe to a topic and receive messages when events are published.
* Example: Order Service → Topic → Inventory Service, Shipping Service, Notification Service.
* Use Case: Real-time updates and event-driven architectures.

𝟑. 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠:
* Real-time event processing and stream management.
* Use Case: Streaming data for real-time analytics, dashboards, and notifications.

𝐓𝐨𝐨𝐥𝐬 𝐭𝐨 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬:

𝟏. 𝐑𝐚𝐛𝐛𝐢𝐭𝐌𝐐 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞:
* Producer sends messages to an Exchange.
* The Broker routes messages to Queues based on routing patterns (Direct, Topic, Fanout).
* Consumer processes the messages from the queues.

𝟐. 𝐊𝐚𝐟𝐤𝐚:
* Kafka allows Producers to publish messages to a Kafka Cluster.
* Messages are stored in Partitions that are replicated across Brokers for fault tolerance.
* Consumer reads messages from these partitions, ensuring efficient message processing.

Messaging patterns like these are fundamental for building distributed, scalable applications. Understanding these can significantly improve the efficiency of your system's communication flow.

Which messaging pattern are you using in your systems?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#MessagingPatterns #RabbitMQ #Kafka #EventDriven #DistributedSystems

𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈: 𝐖𝐡𝐞𝐫𝐞 𝐒𝐡𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐒𝐭𝐚𝐫𝐭?  
Generative AI is a revolutionary field that's reshaping industries by creating new content from existing data.

𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐞𝐦𝐛𝐚𝐫𝐤 𝐨𝐧 𝐭𝐡𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐣𝐨𝐮𝐫𝐧𝐞𝐲:

𝟏. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈?
* Generative AI is a subset of AI that focuses on creating content from existing data.
* Machine Learning (ML) algorithms help the system learn patterns from data and generate new content.
* This field is transforming industries like content creation, customer support, marketing, and more.

𝟐. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐭𝐨 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝
* Probability
* Linear Algebra
* Calculus
* Statistics

These fundamental concepts provide the foundation for understanding how AI models make predictions and decisions.

𝟑. 𝐊𝐞𝐲 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈
* GPT: Language models for text generation.
* Llama: Efficient and lightweight models.
* DeepSeek: For deep learning applications.
* Gemini: Google’s next-gen AI model.
* Claude: AI from Anthropic.

These models are the backbone of modern Generative AI.

𝟒. 𝐊𝐞𝐲 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐒𝐭𝐚𝐜𝐤 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫
* Python: The programming language of choice for AI.
* Langchain: For building AI workflows.
* ChatGPT: One of the most powerful conversational AI tools.
* Prompt Engineering: Crafting the right prompts for better model output.
* Vector Databases: For efficient data retrieval and search.

𝟓. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
* Autonomous Action: AI systems that can make decisions independently.
* Memory: Remembering past interactions.
* Reacting: Responding to changes in real-time.
* Human Control: Allowing users to influence decisions when needed.
* Delegating Tasks: Offloading tasks to AI agents for enhanced productivity.

𝟔. 𝐆𝐞𝐧𝐀𝐈 𝐌𝐨𝐝𝐞𝐥𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧
* GANs, DALL-E, Midjourney, and Flux enable models to generate images from text and create new visual content.

𝟕. 𝐁𝐞𝐬𝐭 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐟𝐨𝐫 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
* DeepLearning AI
* Kaggle
* Google Labs
* Nvidia Learning

Are you ready to dive into the world of Generative AI?

♻️ Repost this to help your network get started
➕ Follow Jaswindder for more

#GenerativeAI #AI #MachineLearning #DeepLearning

𝗧𝗶𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸: 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 𝗹𝗲𝘀𝘀𝗼𝗻 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘄𝗮𝘆 𝗮𝗻𝗱 𝘀𝗵𝗮𝗿𝗶𝗻𝗴 𝗶𝘁 𝗶𝗻 𝗰𝗮𝘀𝗲 𝗶𝘁 𝗵𝗲𝗹𝗽𝘀 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗼𝘂𝘁.

When I began, I jumped straight into modules because that’s what all "𝗿𝗲𝗮𝗹" projects used. Clean structure, reusable code… it looked great.

But in reality? I didn’t understand anything deeply.

One of my colleague once told me: 𝗜𝗳 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗳𝗲𝗲𝗹𝘀 𝗰𝗼𝗻𝗳𝘂𝘀𝗶𝗻𝗴, 𝗮 𝗺𝗼𝗱𝘂𝗹𝗲 𝘄𝗶𝗹𝗹 𝗳𝗲𝗲𝗹 𝟭𝟬𝘅 𝗺𝗼𝗿𝗲 𝗰𝗼𝗻𝗳𝘂𝘀𝗶𝗻𝗴.

He was right. So I paused everything and went back to basics.

I created:
- one S3 bucket
- one EC2 instance
- one IAM role
- one VPC

Nothing fancy.

And suddenly Terraform started making sense — the plan, the state, the errors, the actual flow of infra creation.

Those small wins built my confidence faster than any module ever did.

If you’re new to Terraform, here’s my honest experience:

𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗼𝗻𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲.
𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗶𝘁 𝗳𝘂𝗹𝗹𝘆.
𝗧𝗵𝗲𝗻 𝗺𝗼𝘃𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁.
𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝘀𝗲𝗻𝘀𝗲 𝗻𝗮𝘁𝘂𝗿𝗮𝗹𝗹𝘆 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗿𝗲𝗮𝗱𝘆.

Sometimes the simplest path is the most powerful one.

#Terraform #DevOps #CloudEngineering #InfrastructureAsCode #TechLearning #DevOpsCommunity #CloudComputing #IAC #LearnInPublic #TerraformLearning #CloudBeginners #DevOpsForBeginners #EngineerJourney #StartCoding

𝐓𝐡𝐞 𝐓𝐞𝐜𝐡 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐒𝐞𝐫𝐢𝐞𝐬 (𝐏𝐨𝐬𝐭 #𝟓): 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐈𝐬 𝐉𝐮𝐬𝐭 𝐃𝐞𝐜𝐨𝐫𝐚𝐭𝐢𝐨𝐧.

𝐓𝐡𝐞 𝐦𝐨𝐬𝐭 𝐝𝐚𝐧𝐠𝐞𝐫𝐨𝐮𝐬 𝐟𝐥𝐚𝐰 𝐢𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐢𝐬𝐧’𝐭 𝐝𝐞𝐬𝐢𝐠𝐧. 𝐈𝐭’𝐬 𝐭𝐡𝐞 𝐚𝐛𝐬𝐞𝐧𝐜𝐞 𝐨𝐟 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩. 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐭𝐡𝐢𝐬 𝐭𝐡𝐞 𝐡𝐚𝐫𝐝 𝐰𝐚𝐲.

A few years ago, I walked into an architecture review meeting.
Big screens. Clean diagrams. Perfect slides.

Everything looked world-class until I asked one question: "Who owns what happens when this fails?"

The silence that followed was louder than any design flaw.

The architect looked at the DevOps lead. The DevOps lead looked at the product manager.

And then someone said the words that quietly kill accountability — It’s a shared responsibility.

That’s when it hit me that the shared responsibility often becomes shared confusion.

I’ve seen architecture decks so beautiful they could hang in museums but in production, they collapse at the first sign of chaos.

Not because the design was wrong. But because no one owned what came after.

Architecture is easy when it lives in slides. It’s hard when it lives in production.

When we treat architecture as a diagram instead of a decision, we decorate complexity — we don’t lead it.

💡 Great architecture doesn’t start with patterns. It starts with ownership.

- Who will operate it?
- Who will defend it at 3 AM when it breaks?
- Who will learn from it when it fails?

Because systems don’t fail — ownership does.

The best architecture I’ve seen wasn’t the most elegant — it was the most owned.

✅ Teams who designed with empathy for operators.
✅ Architects who stayed through incidents to learn.
✅ Leaders who tied design choices to measurable outcomes — not slides.

So next time you see a "perfect" architecture, ask: Who’s accountable when this breaks?

Because without accountability, architecture isn’t leadership — it’s decoration.

🚀 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐭𝐚𝐤𝐞? 𝐄𝐯𝐞𝐫 𝐬𝐞𝐞𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐭𝐡𝐚𝐭 𝐥𝐨𝐨𝐤𝐞𝐝 𝐟𝐥𝐚𝐰𝐥𝐞𝐬𝐬 𝐨𝐧 𝐩𝐚𝐩𝐞𝐫 𝐛𝐮𝐭 𝐟𝐞𝐥𝐥 𝐚𝐩𝐚𝐫𝐭 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧?

#TechLeadershipSeries #Architecture #EngineeringLeadership #CloudStrategy #Governance #SystemDesign #SoftwareArchitecture #DevOps #EngineeringCulture #Accountability #Leadership #DigitalTransformation #SRE

taplio

The Battle

Aishwarya Srinivasan

Jaswindder Kummar

Followers

Posts

Engagements

Likes

Performance ScoreAishwarya Srinivasan's LinkedIn Performance

Performance ScoreJaswindder Kummar's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Aishwarya Srinivasan

Jaswindder Kummar

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Aishwarya Srinivasan's LinkedIn Performance

Performance ScoreJaswindder Kummar's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAishwarya Srinivasan's LinkedIn Performance