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

As an AI engineer, LLM Twin is an important architecture to understand.

An LLM Twin refers to a system where a language model is designed to represent the knowledge, writing patterns, and decision logic of a specific person, team, or organization. The goal is consistency and reliability, not novelty.

At its core, an LLM Twin is built around how information flows. Think of it as 4 steps:

1️⃣ First, content is collected from real sources: blogs, internal documents, posts, code repositories, or knowledge bases. This data reflects how the person or organization actually thinks and communicates.

2️⃣ Next, the data is structured for two purposes.
One path prepares instruction-style examples that help the model learn how to respond.
Another path prepares retrieval data so the system can pull relevant context at inference time. This ensures responses stay grounded in real material.

3️⃣ Then comes training and evaluation. Models are fine-tuned where appropriate, tested against clear criteria, and versioned before deployment. This step looks very similar to traditional ML workflows, just adapted for language models.

4️⃣ Finally, at inference time, user requests are combined with retrieved context and passed through the deployed model. Monitoring is critical here: quality, latency, and cost all need to be observed continuously.

Where is this used in practice?

✦ Personal or creator assistants that help scale writing and communication
✦ Company-wide assistants that maintain a consistent product or brand voice
✦ Internal tools that answer questions based on code, docs, or prior decisions
✦ Support systems that reflect how experienced engineers respond to issues

Why should AI Engineers understand this?
This architecture reflects how GenAI systems are actually built and maintained. It forces you to think beyond prompts and focus on data quality, retrieval design, evaluation, and long-term system behavior.

My 2 cents 🤌
This is a GREAT topic to build a portfolio project around!! 

Credit: LLM Engineer’s Handbook by Paul Iusztin  and Maxime Labonne. I really like how this book breaks down AI engineering clearly and goes deep into different GenAI system architectures without hand-waving.

Over the past few months, Arvind and I've delivered seven deep technical workshops across agentic AI, RAG, context engineering, AI evals, MCP, vibe coding, and more.

One theme kept coming up: you want something more structured. Not just standalone sessions, but a clear end-to-end roadmap from A to Z.

So that’s what we’re building.

A structured, comprehensive course for:
- Hands-on builders: software, AI, and ML engineers
- AI product managers
- Tech leaders and CXOs looking to upskill meaningfully

Before we finalize it, I’d love your input. I’ve created a short survey to understand where you are, what you want to learn, and your career goals.

It takes under 10 minutes.

As a thank you, you’ll receive an additional 10% discount when we launch the course soon 🫶

Survey: https://lnkd.in/gQYeBCRP

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Here’s my honest pattern with almost every AI tool I try.

I end up using a lot of tools, usually early access. After repeating this cycle enough times, I realized my reactions are almost predictable.

👉First hour
There’s real excitement.
I’m clicking around, testing odd prompts, trying things that used to fail. No strategy yet, just my curiosity and momentum.

👉Next few hours
I start comparing.
Where would this actually fit in my work?
What would I stop using if I kept this?
Is this clearly better, or just new?

👉Later that day
I experiment without guidance.
I skip the docs and examples on purpose. I try incomplete ideas, messy workflows, prompts I would never put in a demo. This tells me more than any tutorial ever could.

👉Day two
Then I look at the examples the team shared.
They usually work smoothly. They’re polished. They show the tool at its best, which is exactly what they’re meant to do.

👉After a few days
This is where the real understanding kicks in.
I start tweaking those examples. Changing assumptions. Using it in ways that feel realistic, not ideal. That’s when the boundaries show up. Not frustration. Just clarity.

Here’s the takeaway I’ve learned the hard way:
If you haven’t noticed limitations yet, you probably haven’t pushed the tool very far.

And honestly, if I use something consistently for three days and still can’t find a meaningful flaw, that’s rare. That earns real respect.
I haven’t experienced that yet.

Curious how others see this.
Are there tools you’ve used deeply where the limits still haven’t shown up?

Just a couple of weeks ago, Arvind and I hosted an in-person AI meetup with The Hub Bengaluru.

Over 300 people showed up in person.

Every single one of them was either already working in AI or actively trying to break into the space. The energy in the room was unreal. It genuinely felt like a room full of builders, practitioners, and people who are curious enough to go deep.

During the event, we rolled out a small challenge. We asked attendees to share a recap of what they learned, and we said we would pick one winner.

Really excited to share that Suraj V is that lucky winner 🎉
Suraj gets free access to all the The Gen Academy workshops we have hosted so far.

Huge congratulations, Suraj V!! 

Building The Gen Academy has always come from a very real observation that Arvind and I see every single day in our work. The adoption of generative AI and LLMs is moving incredibly fast, but the depth of AI engineering skills needed to build reliable systems is not keeping up.

There is a very real skill gap in AI engineering right now! There are so many curious souls who want to become AI engineers- but they aren't able to get those opportunities; and there are so many companies out there who  want to hire, but aren't finding the right talent. 

Our mission is to bridge that skill-gap!

As people who are deeply involved in this space, we felt a responsibility to help bridge that gap. Not just for people who are already experienced, but also for entry-level folks who want to build strong foundations the right way.

That is why our workshops are intentionally designed to be both intuitive and practical. We focus on theory that actually helps you reason about systems, and we pair that with hands-on demos where you build real things using modern toolkits and frameworks.

So far, we have hosted 6 workshops, and the response has been incredibly encouraging. We have trained 1500+ people already ❤️ 

Our next workshop is coming up on February 15th, and it will be focused on AI evaluations (link in comments)

Do follow The Gen Academy to stay updated on all our upcoming workshops.

Six months ago, Arvind and I started The Gen Academy with one simple goal- to create workshops that are deeply-technical, practical, cuts through the ton of noise out there, and that provides people clarity on building productive-grade AI systems.

So far we have trained 2100+ people. We’ve hosted 7 workshops, and consistently we’ve had 300–400 builders join us live.

The audience has been incredible.
- Software engineers moving into AI.
- AI engineers building LLM systems in production.
- Product leads thinking about reliability and evaluation.
- Directors and VPs from top tech and Fortune 500 companies who want to truly understand what’s under the hood.
- Even people from non-tech background who wanted to strengthen their technical fundamentals 

Recently, we hosted a workshop on AI Evaluations.

And this one felt especially important.

Because evaluating non-deterministic generative systems is fundamentally different from traditional ML evaluation.

We covered:

✦ How to evaluate non-deterministic generative AI systems
✦ How to think about benchmarks and what they actually tell you
✦ How to design your own evaluation dataset
✦ How to choose the right metrics for qualitative and system-level evaluation
✦ How to combine human evals, code-based evals, and LLM-as-a-judge
✦ A hands-on walkthrough so this isn’t just theory

If you’re building LLM applications, RAG pipelines, or agentic systems, evaluation is not optional. It is the difference between a demo and a reliable product.

If you missed the live session, we’ve shared the recording here: https://lnkd.in/gzMdbfWH

We’ll continue building these workshops for engineers who want to go beyond experimentation and into production.

More coming soon, so do follow us & The Gen Academy to get updates on our next workshops.

5 days to go! 
Arvind and I are super excited to be hosting an AI evals hands-on workshop on February 15.

It’s a 90-minute session where we’ll cover the fundamentals of AI evaluations and then actually do it together. You’ll walk away having built and reasoned through real evals yourself. 

If you’re aspiring to be an AI engineer or planning to upskill for AI roles in 2026, understanding how to evaluate LLM applications is critical.

Building an LLM app is often the easy part. Knowing whether it truly works in production is where most teams struggle.

LLM evaluation is not just about correctness anymore. You are judging usefulness, safety, faithfulness to sources, consistency across runs, cost, latency, and real user outcomes across prompts, RAG pipelines, tools, and agentic workflows.

Because this is hard, many teams fall back to vibe-evals. If a few outputs look good, they ship. That approach breaks quickly when models change, prompts regress, retrieval fails, or user behavior shifts. Without evals, problems surface only after users complain.

Why learning evals matters:
✦ Evals turn LLM development from guesswork into engineering
✦ They help catch regressions as models, prompts, or tools evolve
✦ They are essential for scaling RAG systems and agentic workflows
✦ In 2026, strong teams use hybrid evals combining automated metrics, LLM-as-a-judge, and targeted human review

Evals are no longer optional. They are a core AI skill.

In this workshop, Arvind and I will walk through how AI evals are done in real systems, with hands-on exercises so you build intuition.

This is designed for AI engineers and tech leads building systems, and even business leaders who want intuition for what “good” really means in production. Even if you are a beginner, don't worry, we will give you the foundations for you to understand everything comprehensively, and enough resources that you can review at your own pace.

If you want to level up your AI skills for 2026, register here:
https://lnkd.in/dQSR7yhU

Evaluation Resources

Fine-tuning is going to be one of the biggest focus areas for AI engineering teams this year.

We’re past the stage where calling an API is enough. The teams creating real leverage are the ones adapting models to their own data, workflows, and edge cases. Out-of-the-box models are powerful, but they are generic. Fine-tuning is where enterprises unlock meaningful efficiency gains and build real differentiation.

If you want to upskill in AI engineering, this is a topic you cannot skip.
Arvind and I are hosting a deep, hands-on workshop on Fine-Tuning LLMs.

In 90 minutes, you’ll learn:
✦ When fine-tuning makes sense and when it does not
✦ How SFT, RLHF, and DPO fit into real-world pipelines
✦ An end-to-end, code-based demo from raw data to a specialized model
✦ How to evaluate true performance improvements and avoid production pitfalls

This session is designed for software engineers, AI engineers, and data scientists, and it is equally valuable for product managers, solutions architects, tech leads, and executives working closely with model ecosystems.

If you want to move from using LLMs to actually optimizing them, this workshop will give you a practical framework to do it: https://lnkd.in/g-5gHrsY

In 2026, AI isn’t judged by a wow factor anymore.
It’s judged by whether it actually improves your workflows.

One of the most common questions I get from founders and teams I advise is how to automate go-to-market and sales properly. Real GTM automation needs deep ICP research, clear definitions, personalized messaging, and credible sources. This isn’t a one-prompt problem.

Normally, building a solid GTM or sales report would take me several hours, even with existing AI tools helping with research.

I recently got early access to Airtable’s new Superagent, and what impressed me was the approach. It didn’t treat my request as a single task. It broke it into ~16 steps, ran multiple agents in parallel, did deep research per subtask, cited sources, and stitched everything together into one structured output called a Super Report.

I used it to build a technical, informational report on AI for GTM/Sales in minutes. No fluff. No hallucinations. Just deeply researched content I could actually use with business leaders.

If your work involves building reports, decks, or MVP websites that need accuracy and depth, this feels less like “AI that makes things pretty” and more like AI that does real work.

Do check it out here: https://lnkd.in/gDkfSSG2

PS: You can get 2 months free for @Superagent, using code: AISH2FREE

#SuperagentPartner, #ad, #SuperagentbyAirtable

Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2025, 2024
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

The Great Orange Processing Wars: ETL vs ELT vs EtLT

Three factories. Three philosophies. 
One truth most data engineers miss.

𝗘𝗧𝗟 — The Juice Bar
Squeeze before you store. Quality-first, schema-first, compliance-first.

Works perfectly until your business logic changes — then you re-run everything.
Best for: fraud detection, classical ML, regulated industries.
Hidden cost: you can't un-squeeze juice. Raw fidelity is gone forever.
These need strict compliance and clean, pre-processed data before loading.

𝗘𝗟𝗧 — The Warehouse
Store everything raw. Transform strictly on demand. Pure flexibility.

Works perfectly until nobody knows what's in Aisle 47 anymore.
Best for: ad-hoc analytics, LLM training data, fast-moving startups.
Hidden cost: "we'll clean it later" becomes never. Swamps, not lakes.
Here, raw data is loaded first, then transformed as needed—great for massive, unstructured, fast-changing datasets.

 𝗘𝘁𝗟𝗧 — The Gourmet Factory
The lowercase t changes everything.

E — Extract faithfully
t —  Fix only what must be fixed early (PII masking, dedup, type casting)
L — Load into columnar storage
T — Transform richly at query time, per use case

The small t handles what belongs at ingestion: compliance, deduplication, partitioning.
The big T handles what belongs at consumption: business logic, ML features, aggregations.
Best for: RAG pipelines, LLM training, GDPR systems, real-time personalization.
Used, when different data types need different strategies.

The real choice isn’t just technical; it’s about timing.
→ ETL commits early.
→ ELT commits late.
→ EtLT commits minimally early — but maximally late.

That tradeoff shows up everywhere in system design.
Eager vs lazy. 
Compiled vs interpreted. 
Normalized vs denormalized

💡 The Modern Data Engineering Playbook
Stop asking: "ETL or ELT?"
Instead, start asking: "What fuels my AI use case?"
🍊 Traditional ML? → Serve up that perfectly squeezed juice (ETL)
🥤 Exploratory AI? → Keep the whole fruits on hand, blend them later (ELT)
⚡ AI at Scale? → Prepare smartly, create a variety (EtLT)

🎯 The Truth Bomb
AI is like the promised land—bringing automation, intelligence, and scalability.
But without the right pipeline? It’s just chaos.
The best data engineers don’t play favorites.
They choose the right tool for the right need.

What’s your philosophy on data pipelines? 👇
Share it in the comments!

How to Build a Real Data Engineering Project (From Zero)

Step 0: Start with the question
Write one sentence: what decision will this data support, who uses it, and how often it updates.
Example: “Track daily sales and flag unusual drops.”

If you can’t answer this, stop.

here’s the roadmap that actually works:
 
Step 1: Pull in messy data
Use raw inputs: CSVs, public APIs, or open datasets from Kaggle, data.gov, or GitHub.
The mess is the point.

Step 2: Read before you write
Open the data in Pandas or a notebook.
Check columns, types, missing values, and duplicates.
This step saves hours later.

Step 3: Clean and shape
Fix types, handle nulls, standardize values, and add derived fields.
Use SQL and Pandas. Tools matter less than clear logic.

Step 4: Store it simply
Pick one place as your source of truth: DuckDB, SQLite, or Postgres.
Design tables well. Don’t chase big platforms yet.

Step 5: Build the pipeline
Start with plain Python scripts.
Schedule with cron.
Only add Airflow or Prefect after it works end to end.

Step 6: Add checks
Row counts, null checks, freshness.
If data breaks silently, the project fails.

Step 7: Show the output
One dashboard or query layer using Metabase, Power BI, or Looker Studio.

Step 8: Ship it properly
GitHub repo, README, diagram, run steps, and assumptions.

Step 9: Improve
Add incremental loads, logging, schema handling, and faster queries.

The best part? Every single tool in this stack has a free tier.

If you are someone, who wants to explore your expertise and expand your data engineering portfolio, grab a look at these projects:

1. Build a simple ETL pipeline using Python and SQLite : Ankit Bansal - https://lnkd.in/gTdCV9aJ

2. End To End ETL Pipeline Using AirFlow And Astro : Krish Naik - https://lnkd.in/gusmJNfs

3. Data-Engineering-Project-with-HDFS-and-Kafka : Ahmet Furkan DEMIR -https://lnkd.in/gkw4wYSC

4. Develop a data warehouse solution using Snowflake, dbt : Shashank Mishra 🇮🇳https://lnkd.in/gf-c5TR7

5. Amazon Data Analysis Using Databricks and Snowflake: Kinzorize - https://lnkd.in/gUDiwYpQ

6. Uber Data Analytics using GCP: Darshil Parmar - https://lnkd.in/gqE-Y4HS

7. Pyspark Project : Alex Ioannides, PhD - https://lnkd.in/g-T_ZqXu

8. Apple Data Analysis(Spark) project : Ankur Ranjan - https://lnkd.in/gt9JAkRt

9. Airflow data pipeline with BigQuery, dbt Soda, and more - Marc Lamberti https://lnkd.in/g8xjqisu

10. AWS End to End Data Engineering : Yusuf Ganiyu - https://lnkd.in/gtj-tqWK

The engineers who move fast aren’t the ones with the best tools. 
They’re the ones who stop waiting for perfect and start building.

The Iceberg nobody shows you about Data Engineering!

Data engineers don't surface level things like,
Spark and Kafka and Snowflake
$150k salary dreams
Building ML pipelines
Modern data stack vibes

Here’s how the life of a ℝ𝔼𝔸𝕃 𝔻𝔸𝕋𝔸 𝔼ℕ𝔾𝕀ℕ𝔼𝔼ℝ looks like  😮‍💨
(The brutal truth nobody tells you)

1️⃣ 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗵𝗮𝗻𝗴𝗲𝗿: “I’ll master Spark, Kafka, and Snowflake in 3 months and land a $150K job!”
2️⃣ 𝗝𝗼𝗯 𝗛𝘂𝗻𝘁𝗲𝗿: “Why do they want 5 years experience for an ‘entry-level’ position? And what’s a ‘take-home coding challenge’?”
3️⃣ 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲: “I spent 40 hours building their pipeline assignment. They ghosted me after the final round.”
4️⃣ 𝗝𝘂𝗻𝗶𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “The production data is nothing like the tutorial datasets. Why is everything broken and undocumented?”
5️⃣ 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “I’m not building ML models. I’m fixing CSV parsing errors and explaining why dashboards show different numbers.”
6️⃣ 𝗦𝗲𝗻𝗶𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “60% of my time is meetings about data governance. The other 40% is arguing with stakeholders about SLAs.”
7️⃣ 𝗟𝗲𝗮𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “The pipeline that worked for 2 years suddenly failed. Nobody knows why. The person who built it left 6 months ago.”
8️⃣ 𝗦𝘁𝗮𝗳𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “I spend more time in Slack explaining why data is 2 hours late than actually engineering. Also, our AWS bill is $50K this month.”
9️⃣ 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁: “I designed the perfect architecture. Nobody followed it. Everything runs on Excel sheets shared via email. I’ve accepted this.”
🔟 𝗖𝗧𝗢 (𝗳𝗼𝗿𝗺𝗲𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿): “We need to be more data-driven. Also, can you build this by Friday? And make it real-time. Budget is $500.”

The real job description: 
• 20% building cool stuff 
• 80% debugging, meetings, and existential crisis

What they don’t tell you:
→ You’ll become a CSV therapist
→ “Data quality” means fixing the same broken pipeline every week
→ The hardest part isn’t the tech—it’s managing expectations
→ It's about business chaos and technical constraints
→ Data quality isn't a tool. It's an ownership issue
→ Most failures aren't technical. They're communicational
→ Scalability is easy to achieve. Designing and enforcing are two different things
→ The more senior you get, the less you're involved

The most challenging production bug is about stakeholder misalignment.
"Real-time" data usually means we didn't plan for this. 

Data engineers, put differently, are part therapists, detectives, and negotiators.

And despite the 2AM alerts, data pipeline issues and never ending slack threads —

It’s a privilege because in the end, 
the pipeline runs, 
the dashboard matches,
… And leadership finally trusts the numbers. Pure magic ✨

But here’s the thing… Despite all this, we still love what we do.

Do you remember, when did you automate your first pipeline?

Dear Data Engineers — meet your new coworker. It's not human.
Monday, 9:47 AM. Analyst pings: "orders_final is missing 10K rows."

You: Open Airflow. Query source. Compare counts. Find schema drift. Manual backfill. 45 minutes. Annoyed.
The Agent: Queries metadata → checks logs → detects schema drift → generates fix → asks "Run this?" 90 seconds. Done.

Here's the analogy that makes it click:

AI Agents = Self-modifying Airflow DAGs
𝖸𝗈𝗎 𝗁𝖺𝗋𝖽𝖼𝗈𝖽𝖾: 𝖾𝗑𝗍𝗋𝖺𝖼𝗍() → 𝗍𝗋𝖺𝗇𝗌𝖿𝗈𝗋𝗆() → 𝗅𝗈𝖺𝖽()

Agents do:
𝗐𝗁𝗂𝗅𝖾 𝗇𝗈𝗍 𝗌𝗈𝗅𝗏𝖾𝖽:
  𝗍𝗁𝗂𝗇𝗄() → 𝗉𝗂𝖼𝗄_𝗍𝗈𝗈𝗅() → 𝖾𝗑𝖾𝖼𝗎𝗍𝖾() → 𝖾𝗏𝖺𝗅𝗎𝖺𝗍𝖾()

Your DAG just became sentient and stopped asking for help.

The good news? You already have 80% of the skills.

Your Python scripts ARE the tools. You just need to make them agent-callable — add clear docstrings, wrap them as functions, expose them to the loop. 

The real shift isn't technical. It's mental.

Before: you were the pipeline. 
After: you design the system that runs the pipeline.
Before: 2 AM Slack pings wake you up. 
After: the agent handles it. You review the decision over morning coffee.

You're not being replaced. You're being scaled.
One DE + three agents = a five-person team's output.
Agents handle 2 AM fires. You architect system!

Working with AI Agents? Here are a few rules I always find myself returning to.

Do's:
✅ Debug it weekly? Automate it. Agents thrive on tools, not just shared knowledge.
✅ Document as if the reader knows nothing. Trust me, the agent definitely doesn’t.
✅ Record every decision made by agents. When things go wrong, you’ll want a clear trail.

Don'ts:
❌ Granting write access on Day 1. Start with read-only. Trust is something you build over time.
❌ Skipping human checkpoints on critical operations. Always gate anything that changes data.
❌ Assuming agents understand your context. They don’t. Make it crystal clear.

What’s the takeaway?
Agents reflect your habits—both the good and the bad.
So, build cleanly. Document thoroughly. Keep communication open where it counts.

You were building systems for humans,
Now build them for Agents.
This post is inspired by Vu Trinh's article on AI Agents. Find the link below. 👇

🌊 A fish thinks water needs it. Water thinks it needs the fish.

Then the net drops.

The fish gets captured. 
The water? It simply flows through.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗔𝗜 𝘁𝗼𝗱𝗮𝘆.

Raw data thinks AI can't survive without it. 
AI systems think any data is worth training on.

Then comes the real test — production.

And suddenly:

🐟 Ungovernated data gets 𝘁𝗿𝗮𝗽𝗽𝗲𝗱 in compliance failures
🐟 Unobserved pipelines get 𝗰𝗮𝘂𝗴𝗵𝘁 in silent corruption
🐟 Poor quality features get 𝗻𝗲𝘁𝘁𝗲𝗱 by model drift
But the water — the Governance framework — just flows right through.

It was never enslaved by bad data.

Here's the truth nobody tells you:
Most organizations treat data like fish — abundant, assumed, and disposable.

The ones building real AI treat data like whales.

Whales can't be netted…
They're too governed, too observable, too quality-conscious to be trapped by broken systems.

The 3 pillars that separate fish from whales:

⚙️ 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 = The ocean itself (Ownership, lineage, policy — the environment data lives in)

👁️ 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 = Knowing where the whale is (Freshness, volume, schema, distribution — real-time awareness)

✅ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 = The whale's immune system (Completeness, accuracy, consistency — what makes it survive production)

The ocean doesn’t mourn the fish lost to the net.
It only protects what chose to become sovereign.
Your data can survive capture — or it can command the ocean.

𝐴𝐼 𝑑𝑜𝑒𝑠𝑛’𝑡 𝑓𝑎𝑖𝑙 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑓 𝑏𝑎𝑑 𝑚𝑜𝑑𝑒𝑙𝑠. 𝐴𝐼 𝑓𝑎𝑖𝑙𝑠 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑠 𝑓𝑖𝑠ℎ 𝑓𝑜𝑟 𝑑𝑎𝑡𝑎 𝑖𝑛𝑠𝑡𝑒𝑎𝑑 𝑜𝑓 𝑟𝑎𝑖𝑠𝑖𝑛𝑔 𝑤ℎ𝑎𝑙𝑒𝑠.

💬 Are you fishing for data or raising whales?
#AI #Data

The GAP is real
Every common person is equally concerns about AI.
"Instagram/ Siri is hearing me and changing the recommendations"

One of my niece said this to me yesterday.
And.. I really felt like we're just on the different sides of the screen.

The irony?
We pull all-nighters worrying about data security...
They pull all-nighters worrying about data security.

What we're actually doing?
 → Building trust & safety frameworks
 → Implementing data privacy by design
 → Creating quality checks on quality checks
 → Making AI explainable and auditable

What people think we're doing?
 → Building Skynet
 → Stealing their job
 → Teaching Alexa to spy on them
 → Making everything too complicated on purpose
 
To get the real time understanding, these are some amazing blogs to read:
1. Seattle Data Guy - https://lnkd.in/g6kC-9wa
2. Data Engineering Things - https://lnkd.in/gg_hCpk7
3. DataExpert.io - https://lnkd.in/gxj-wAHD
4. Confessions of a Data Guy - https://lnkd.in/gXnAFW8g
5. Netflix Tech - https://lnkd.in/gUMZ9xpS
6. Engineering at Meta - https://lnkd.in/gg7k9pWh
7.  Spotify - https://lnkd.in/gJWShJKV
8. LinkedIn Engineering - https://lnkd.in/gw6aBvYn
9. Slack Engineering - https://slack.engineering/
10. Uber Engineering - https://lnkd.in/gyUdJcMb
11. PayPal Tech - https://lnkd.in/gdmiErJY
12. Airbnb Engineering - https://lnkd.in/gQVYUeJY
13. Databricks - https://lnkd.in/gD24FB63
14. Canva Engineering - https://lnkd.in/gYA8eUnQ
15. Jesse Anderson - https://lnkd.in/gYaS58E4
16. Andreas Kretz - https://lnkd.in/gWTNDwUg
17. DataCamp - https://lnkd.in/gEen6W7U
18. Data Engineering Weekly - https://lnkd.in/g7CA7tHM
19. The Analytics Engineering Hub - https://lnkd.in/gyMrwzPM
20. Atlassian Engineering - https://lnkd.in/grvHZskQ

The gap isn't just technical - it's educational, emotional and human.
Let's make AI less scary and more simplified as a support system.

Engineers can build amazing things. 
But we also need to bridge the gap between "𝗵𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀" and "𝘄𝗵𝘆 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗮𝗿𝗲."

So, where are you right now — building it, learning it, or still figuring out what all the hype is about? 💬

Communication is the key - From raw data to profitable outcomes!

It’s the TRUST,
With CLEAN outcomes,
To deliver solutions with IMPACT!

…. That helps you refine — Raw data into reliable solutions to deliver PROFIT.

Most data and AI teams are sitting on a treasure trove — and they might not even realize it.  

What separates a $1M budget line from a $10M+ profit powerhouse? 
It’s not just your algorithm or your tech stack. It’s all about how you navigate through these five stages:  

1️⃣ RAW DATA = The Ore It’s messy, unstructured, and often trapped in databases that no one fully trusts. Every company has it, but very few know how to refine it.  

2️⃣ SORTED = Refined Ingots Now we’re talking! Cleaned, labeled, and structured — think Metal, Copper, Gold, Silver. It’s got form, but just having form doesn’t mean it’s market-ready.  

3️⃣ ENGINEERED AI = The Component This is where your machine learning models, logic layers, and automation triggers come into play. Your engineers have crafted some impressive parts, but they’re still just that — parts. 

4️⃣ INTEGRATED SOLUTION = The Product Costs drop ↓ and revenues rise ↑. Now you’ve got something the market can actually USE. This is where the $10M+ potential lies.  

5️⃣ EXPLAINED STORY = The ROI “We transformed from a $1M cost center into a $10M profit machine.” This is the kind of statement that gets budgets approved, deals sealed, and careers launched.  

Here’s the hard truth:  
Most engineers stop at Stage 3, 
while many managers only get to Stage 4. 

The true winners — the leaders who really excel — are the ones who thrive at Stage 5.  

Keep this in mind:
Communication transforms beans into drinks.
Trust transforms drinks into habits.
Impact transforms habits into profits.

Stop building houses. 
Start constructing Profit Factories.  
That’s the 10X leap.  

♻️ Save this if you’re leading a #data or #AI team. 
💬 Which stage is your team currently stuck in? Let us know in the comments!

When DATA GOVERNANCE comes as an afterthought, you're literally screwed!

Agree?
Ignoring foundations that builds data trust can be painful.

Someone once asked me — Pooja, do you really think a simple data pipeline could cost such a huge bill?

Absolutely, it can. 
Data solution that is easy to represent.
But, here's the common pitfall many teams encounter:

→ The pipeline looks pristine on a whiteboard
→ It’s easy to draw. Easy to demo.
→ But scaling it? That’s a whole different ballgame. Trusting it? Even harder.

Here's what nobody tells you right off the systems:

📌 A sleek diagram ≠ a dependable data system
📌 Quick data movement ≠ accurate data movement
📌 More pipelines without governance = more confusion, not more clarity

What does DATA GOVERNANCE truly offers the teams?

✅ Everyone knows "who’s responsible" for what data
✅ No more debates over "which version is the right one?"
✅ Teams spend less time troubleshooting — and more time making "decisions"
✅ Your data builds "trust". Both internally and externally.

What’s the real price of skipping it?

It’s not just about the money.
It’s about team frustration.
It’s about flawed decisions.
And it’s about data that no one trusts.

Governance isn’t just a tech problem.
It’s a people + process + discipline problem.

Build the foundations before you start building floors.

Start small, but make it intentional.
Be honest, don't let a chat with the CFO at 9 AM cause chaos.
The $1150K surprise bill on a Monday morning can be thrilling over the week 😬

Here’s are some amazing folks to follow and learn more on Data and AI governance —

What's your perspective data leaders? 
George Firican 
John Wernfeldt 
Clare Kitching 
Charlotte Ledoux  
Dylan Anderson  
Piotr Czarnas 

What’s the one governance gap your team keeps revisiting?
Share it below 👇

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