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 ♥️

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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. 

〰️〰️〰️
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HiCounselor

Chief Executive Officer

Deloitte Digital

• Led a team of 5 engineers and collaborated with cross-functional teams across 3 global locations to launch a Sales Cloud platform for a Fortune 500 client enabling them to grow revenue in Europe by $50M+.
• Developed new payment integration methods (APIs) for SMBs to use PayPal, increasing the company’s mobile payment share by 8%, generating $20B+ in payment volume.
• Collaborated with design team to perform customer discovery, develop prototypes with
wireframes, conduct experiments, and establish KPIs to validate user flows and new product ideas. Achieved 65% retention rate through features like personalized content lists, certifications and activity feeds.
• Implemented 18-month product roadmap in collaboration with engineering team. Built low-cost platform by leveraging open-source technologies. Designed a bandwidth-efficient solution to distribute content to under-connected teachers across the US.

Salesforce Technology Consultant

•	As a Service Cloud Consultant, my role is to analyze the information gathered from client, and studying the procedures and systems currently in place to drive the required solution and meet project's demand.
•	Led multiple discovery sessions and conducted business user group workshops with clients to gather business and design requirements.
•	Coordinated with PwC offshore team (Service Delivery Center, India) to handle Salesforce Administration, Configuration and Implementation for customer care side in accordance with application requirements to satisfy complex business process automations. This includes designing Custom Formula Fields, Field Dependencies, Validation Rules, Work Flows, and Approval Processes for automated alerts, field updates, and Email generation.
•	Designed Process Flow Diagrams to represent client’s customer service and support process using MS Visio.
•	Developed custom reports and dashboards in Salesforce for different users in the organization as per business requirements.
•	Built Functional Requirement Document (FRD) and Functional Design Document (FDD) as client deliverables.
•	Presented customer care work stream and demonstrated technical functionality in Salesforce during end to end playback for 160 client representatives.
•	Worked closely with change management and operational readiness team to develop customer care training modules.
•	Conducted Train the Trainer (TTT) session to provide walk through of developed system and functionality in Salesforce.

Experienced Associate, Technology Consulting

16th Annual Duke Start-up Challenge 2015

•	Edulyf was one of the Top 8 finalist in 16th Duke Startup Challenge 2015 and applauded by judges for its idea and initial traction. 
•	Led a team of 24 on an online education startup idea with objective to improve education standard in India targeting 31.1 million students and 95k schools.
•	Developed a prototype of startup idea and prepared a comprehensive business plan for Round 3 
•	Represented my team in finale and video of investor pitch explaining the business plan, achievements till date, funding requirement and future vision. Find video at http://bit.ly/1ABQWFG

Founder, Edulyf

Parsons Corporation

- Conducted market research, expert interviews, data collection and analysis, SWOT analysis and competitor analysis to generate a thorough performance report for Parsons in social media within the AEPCM (Architecture, Engineering, Procurement, and Construction Management) industry
- Researched and documented industry best practices and compared the best practices to Parsons’ social media performance
- Developed targeted social media strategies for major clients (federal government, state government and private companies), suppliers, and potential employees for the company to achieve a better performance, reach wider audience and create potential leads in the future

Digital Marketing Manager

Undercover Colors

•	Researched Five Factor Analysis including IP-Strength, Market Attractiveness, Level of Development, Ease of Implementation and Timing for launch of Undercover Color’s product (Date Rape Drug Detector Nail Polish). Hence, summarized current state of company’s product with major strengths and weaknesses.
•	Analyzed profiles of four major potential corporate partners and distributors of nail polish including Essie, OPI, Orly and Kirkir through secondary market research to know about their acquisition and merger behavior with start-up companies.
•	Developed Value chain and conducted 12 interviews of experts from Universities, Nail Industry and Contract Manufacturing industry to know their viewpoint about date rape drug detector nail polish
•	Carried out customer survey with 264 people through social media and primary marketing research to know about customer’s expectations about average price, interest in product, usage, preferred channels of purchase and strength of slogan.
•	Suggested future plan for next 18 months including total expected cost and timeline, lab tests required, preferred marketing channels and sustainability as private label.

Business Development Manager

Ericsson

•	Analyzed EMF Health Check Report for existing Mobistar sites and made it compliant according to BIPT (Belgium Institute of Postal Services and Telecommunications) Standards.
•	Carried out optimization techniques (mechanical/electrical tilting, appropriate transmit power setting, antenna clearance, neighbor list analysis, etc.) for optimal site/cluster performance.
•	Analyzed critical points under site range with high electro-magnetic field (EMF) radiation and suggested plan to optimize it for potential human exposure

Radio Frequency Engineer

Dr. B R Ambedkar National Institute of Tchnology, Jalandhar ( PUNJAB)

•	Conducted presentation of technical and cultural fest in more than 35 different engineering schools in four cities in Northern India. 
•	Led team of 26 members for digital marketing of technical fest (Techniti 2011) and cultural fest (Utkansh 2012) of my undergraduate school. Personally trained 12 team members about digital marketing. Convinced dean of school to allot extra computer lab for two months after office hours.   
•	As a collective team effort, number of participants was increased by 18% in Techniti 2011 and by 23% in Utkansh 2012.

Head, Brand Building and Promotions Committee

Defence Research and Development Organisation

•	Designed hardware of “16x2 LCD interfacing with 8051 microcontroller” to display message for any instrument.
•	Programmed “16x2 LCD interfacing with 8051 microcontroller” using Express PCB software.

Microcontroller 8051 Intern

HPCL

•	Analysed applications of Distributed Control System (DCS) in the Petroleum Industry
•	Managed Distributed Control System (DCS) using Experion Software
•	Monitored different Tank Gauging Systems in petroleum refinery, hence measured parameters like temperature, pressure, density and height.

Distributed Control System Intern

CEO @ HiCounselor | 2X O-1 Visa Holder | Ex-Deloitte, PwC | Building a Community to Help Job Seekers with Referrals

I came to the US in 2014 for a master's degree at Duke University.

Post-graduation, I was fortunate to start my career as a Tech Consultant at PwC and then with Deloitte in San Francisco. 

However, I always wanted to utilize my tech skills to start my own business. 

In 2020, I decided to leave my job and launched HiCounselor in San Francisco.

The decision wasn't easy as I had many open questions, concerns about immigration, and fear of failure. 

Fortunately, the decision turned out to be the right one. 
✅ Generated millions in revenue
✅ Raised VC funds from top investors in Silicon Valley
✅ Employ more than 40 people in the US, Canada, and India.

👉 These four years have taught me a lot about starting a company as an immigrant in the US, and I want to share this journey with every technical professional who wants to make side income or become a full-time entrepreneur.

✉️ Join my newsletter (https://lnkd.in/gPBziwZ3), where I share weekly actionable tips. 


👉  If you are not ready to leave your full-time job at this time, that's okay.
You can join hicounselor.com/mentor and make a side income by sharing your technical expertise. 

If you need any help, feel free to message me or comment on my posts. I will respond as soon as possible.

Aditya Sharma

𝗔𝘀 𝗮𝗽𝗽𝘀 𝗺𝗼𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗱 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝘀𝗰𝗮𝗹𝗲, 𝗼𝗻𝗲 𝘁𝗿𝘂𝘁𝗵 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁.
There is no longer a single database that fits every use case.

It’s not SQL vs NoSQL anymore.
It’s about choosing the right database for your data and your workload.

𝗟𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁 👇

𝟭. 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 (𝗦𝗤𝗟) 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Structured, reliable, and great for transactions.
𝗨𝘀𝗲𝗱 𝗶𝗻: Banking, ERP, Order Management
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: MySQL, PostgreSQL, SQL Server


𝟮. 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Designed for analytics and lightning-fast aggregations.
𝗨𝘀𝗲𝗱 𝗶𝗻: Data Warehousing, BI, OLAP
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Redshift, ClickHouse, HBase


𝟯. 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Handle data that changes over time — metrics, events, IoT signals.
𝗨𝘀𝗲𝗱 𝗶𝗻: Monitoring, IoT, Real-Time Analytics
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: InfluxDB, TimescaleDB, QuestDB


𝟰. 𝗜𝗻-𝗠𝗲𝗺𝗼𝗿𝘆 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Everything runs in memory → ultra-low latency responses.
𝗨𝘀𝗲𝗱 𝗶𝗻: Fraud Detection, Real-Time Apps, Caching
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Redis, SAP HANA, Ignite

𝟱. 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Perfect when relationships between data matter most.
𝗨𝘀𝗲𝗱 𝗶𝗻: Social Networks, Recommendations, Knowledge Graphs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Neo4j, JanusGraph, Cosmos DB


𝟲. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Flexible and great for unstructured or semi-structured data.
𝗨𝘀𝗲𝗱 𝗶𝗻: CMS, User Profiles, Mobile Apps
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: MongoDB, Couchbase, Firestore


𝟳. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ The new stars of AI — store embeddings for smart, semantic search.
𝗨𝘀𝗲𝗱 𝗶𝗻: Generative AI, RAG, Similarity Search
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Pinecone, Milvus, Weaviate


𝟴. 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Store data as objects that match your code directly.
𝗨𝘀𝗲𝗱 𝗶𝗻: CAD/CAM, Complex Engineering Systems
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: db4o, ZODB

𝟵. 𝗕𝗹𝗼𝗰𝗸𝗰𝗵𝗮𝗶𝗻 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ Decentralized, tamper-proof, and transparent.
𝗨𝘀𝗲𝗱 𝗶𝗻: Supply Chain, Identity, Provenance Tracking
𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: BigchainDB, CovenantSQL

𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀: 

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄: 
Elise Victor, PhD
Yuma .
Sol Rashidi, MBA
Cornellius Y.
Sairam Sundaresan
Aishwarya Srinivasan

♻️ Please 𝗥𝗲𝗽𝗼𝘀𝘁 or 𝗦𝗵𝗮𝗿𝗲 to help others stay informed

Want to stay updated on the latest AI Tools and AI Agents? Join my free AI WhatsApp community 👇
https://lnkd.in/epHZYb-j

#AI #SQL #NoSQL

#AIForEveryone | Learn AI with Me | AI Tools • AI Agents • AI News | 160k+ Followers | Ex-Deloitte & PwC

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗮 𝗴𝗲𝗺 𝗳𝗼𝗿 𝗮𝗻𝘆𝗼𝗻𝗲 𝘄𝗮𝗻𝘁𝗶𝗻𝗴 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲.

If you’ve been wondering how to actually build AI Agents, this could be your real starting point.

This new “AI Agents for Beginners” playlist is your hands-on guide from 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 → 𝗰𝗼𝗱𝗲.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻:

↳ What AI Agents are & how they work
↳ Framework Semantic Kernel
↳ Design & planning patterns for agents
↳ Tool use, RAG & multi-agent systems
↳ Deploying, improving & adding agent memory

Want to master AI Agents and AI Tools? Check this: https://lnkd.in/eayn5Cnt


𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗺𝗼𝗿𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀! 👇

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄:
Shivani Virdi
Andreas Horn
Armand Ruiz
Michael Kisilenko
Lee Boonstra
Miku Jha

#AIAgents #MicrosoftDeveloper #AgenticAI

Nearly every company using RAG today is building customer chatbots, internal assistants, or support automation.

And honestly, open source RAG makes this possible for everyone.

𝗟𝗲𝘁'𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝗥𝗔𝗚 𝗶𝗻 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘄𝗮𝘆 :

𝟭. 𝗜𝗻𝗴𝗲𝘀𝘁 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
↳ This is where you collect your data and clean it.
↳ Think of it as preparing the information so the system can actually use it.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: Kubeflow, Airflow, LangChain loaders


𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗥𝗮𝗻𝗸𝗶𝗻𝗴
↳ When a user asks something, this layer finds the most relevant piece of information.
↳ It decides what matters right now.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: Elasticsearch, Weaviate, FAISS

𝟯. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀
↳ These models turn text into vectors.
↳ This is basically converting words into meaning that the system can search.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: Sentence Transformers, JinaAI

𝟰. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ This is where all those vector representations are stored.
↳ So when you search, it responds quickly and accurately.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: Milvus, Chroma, Qdrant

𝟱. 𝗟𝗟𝗠 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 / 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻
↳ This defines how everything flows together.
↳ It connects each part so the system behaves intelligently.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: LangChain, LlamaIndex

𝟲. 𝗟𝗟𝗠𝘀 (𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗧𝗵𝗮𝘁 𝗥𝗲𝘀𝗽𝗼𝗻𝗱𝘀)
↳ This is the part that generates the final answer in natural language.
↳ It uses the retrieved context to avoid hallucination.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: LLaMA, Mistral, DeepSeek

𝟳. 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗟𝗮𝘆𝗲𝗿
↳ The interface where users interact.
↳ This could be a chatbot, dashboard, or app.
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀: NextJS, Streamlit

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄:
Sol Rashidi, MBA
Cornellius Y.
Sairam Sundaresan
Aishwarya Srinivasan
Jerry Liu

Want to learn AI Agents and AI Tools from me? Check this: https://lnkd.in/eayn5Cnt

♻️ Please 𝗥𝗲𝗽𝗼𝘀𝘁 or 𝗦𝗵𝗮𝗿𝗲 to help others stay informed

#LangChain #RAG #OpenSource

Learn AI with Me | AI Tools • AI Agents • AI News | 160k+ Followers | Ex-Deloitte & PwC

If I want to become an AI Engineer in 2025–26, this is the exact roadmap I’d follow.

Because becoming an AI Engineer isn’t about jumping straight into LLMs.


𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝟭𝟬-𝘀𝘁𝗲𝗽 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀:

𝟭. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
Learn syntax, OOP, data structures, and version control.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/eFRm6NZg


𝟮. 𝗠𝗮𝘁𝗵 & 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗔𝗜
Linear algebra, probability, calculus, distributions.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/eAR2bMbf


𝟯. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀
Regression, classification, clustering, evaluation.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/egymXERt


𝟰. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Neural networks, CNNs, RNNs, optimizers, regularization.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/esCFihGq


𝟱. 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (𝗡𝗟𝗣)
Tokenization, embeddings, sequence models.
𝗦𝗼𝘂𝗿𝗰𝗲:https://lnkd.in/eqJ_gwQT


𝟲. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗟𝗠 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀
Self-attention, encoder/decoder models, GPT, BERT, T5.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/e67ggkd6


𝟳. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗖𝘂𝘀𝘁𝗼𝗺 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴
Parameter-efficient training, quantization, optimization.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/ems5SK9r


𝟴. 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸
Build LLM apps, pipelines, and retrieval workflows.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/eX9RVD82


𝟵. 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 & 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
Multi-node reasoning, retrieval automation, orchestration.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/e_yQ2_pq


𝟭𝟬. 𝗠𝗖𝗣 + 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
Build autonomous AI agents that plan + act across systems.
𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/eVHgeFit


This roadmap takes you from fundamentals → real-world AI engineering.

If you're aiming for roles like:
• AI Engineer
• Machine Learning Engineer
• LLM Application Engineer
• AI Research

Want to learn AI Agents and AI Tools? Check this: https://lnkd.in/eayn5Cnt

𝗧𝗼 𝗹𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗳𝗼𝗹𝗹𝗼𝘄:
Andrew Ng
Aravind Srinivas
Ashish Vaswani

♻️ Please 𝗥𝗲𝗽𝗼𝘀𝘁 or 𝗦𝗵𝗮𝗿𝗲 to help others stay informed

#LLM #AI #MachineLearning

Shivam Singh, Aditya Sharma and 57 other attendees

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