My missions
→ Lead & ship production-grade AI/ML systems - including AI Agents & RAG that actually move business metrics.
→ Educate AI/ML practitioners on how real-world production AI/ML systems are designed, deployed, and maintained (the parts most people skip).

My portfolio
→ Senior Data Scientist / AI Engineer (8+ years)
→ Built an AI Agent–powered FAQ chatbot (LangChain/LangGraph, AWS) used by thousands; significantly reduced customer support load.
→ Built end-to-end RAG: ingestion, semantic/recursive chunking, retrieval strategies, GDPR-first data flows
→ Built CLV forecasting, recommendation systems, marketing optimization, and scalable MLOps systems with significant business impact

My newsletter
→ JAM with AI - read by 26,000+ AI/ML builders
→ jamwithai.substack.com

Work with me
→ Collaborations, talks, and mentoring.

I've read 25+ AI/ML books in 8 years. 

Only 11 would actually move the needle in production.

(I've shipped Data Science, MLOps, RAG systems, and AI agents)

Here's what actually matters:
📌 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
→ Mathematics of Machine Learning - Tivadar Danka
Understand the foundations with great intuitions!
🔗 Link - https://lnkd.in/d3g494VJ

→ ML with PyTorch and Scikit-Learn - Sebastian Raschka, PhD
Bridge theory to practice with both classical ML and deep learning.
🔗 Link -  https://lnkd.in/dkBwkjBc

📌 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗦𝘁𝗮𝘁𝘀
→ Fluent Python - Luciano Ramalho
Write production-grade Python code.
🔗 Link - https://lnkd.in/dPzSFYmr
(skip it if you think you can learn better through courses)

→ Practical Statistics for Data Scientists - Bruce & Bruce
A/B test models and detect data drift.
🔗 Link - https://lnkd.in/dwtNNU4R

📌 𝗡𝗟𝗣 
→ Python NLP Cookbook - Antić & Chakravarty
Classical NLP for when RAG retrieves garbage.
🔗 Link -  https://lnkd.in/dVPGPew6

→ NLP with Transformers - Tunstall, von Werra, Wolf
Understand what's under the hood of LLMs.
🔗 Link - https://lnkd.in/dqk62AQT

📌 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
→ Designing Machine Learning Systems - Chip Huyen
Be in the 13% that reach production.
🔗 Link - https://lnkd.in/dPXxKNgk

→ ML System Design Interview - Aminian & Xu
Think in patterns: scale from 1K to 100M users.
🔗 Link - https://lnkd.in/d_V98BW9

📌 𝗟𝗟𝗠𝘀 & 𝗔𝗴𝗲𝗻𝘁𝘀
→ AI Agents in Practice - Valentina Alto
Build agents that work in production.
🔗 Link -https://lnkd.in/d92bycbU

→ AI Engineering - Chip Huyen
LLMs in production: RAG, prompts, infrastructure.
🔗 Link - https://lnkd.in/dw_F-PtH

📌 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲
→ Designing Data-Intensive Applications - Martin Kleppmann
Your model is only as good as your infrastructure.
🔗 Link - https://lnkd.in/duWW2tD6

𝗠𝘆 𝗵𝗼𝗻𝗲𝘀𝘁 𝘁𝗮𝗸𝗲:
Pick 1-2 books. 
Build something small. 
Come back when you hit problems.

Detailed post on WHEN each saved me → Link in comments 👇

𝗪𝗵𝗮𝘁 𝗯𝗼𝗼𝗸𝘀 𝘀𝗵𝗮𝗽𝗲𝗱 𝘆𝗼𝘂𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆?

PS: There are many books out there, but these are the list of must read from my end. For learning LLMs as well there are multiple resources, so didnt include. 

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Shirin Khosravi Jam

Sr. Data Scientist/ AI Engineer | Data Science, RAG, AI Agents, & MLOps | Germany’s Top Female Voice in AI (Favikon) 🇩🇪

Stop using just Kaggle for ML data

7 real-world data sources for your Machine Learning projects:

𝟭. 𝗦𝘁𝗿𝗮𝘁𝗮𝗦𝗰𝗿𝗮𝘁𝗰𝗵
→ 49+ real business datasets
→ Actual interview-style problems
→ https://lnkd.in/dkscDjvQ

𝟮. 𝗨𝗖𝗜 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆
→ 653+ datasets, battle-tested
→ What researchers actually use
→ https://lnkd.in/dzCUAFpH

𝟯. 𝗔𝗪𝗦 𝗣𝘂𝗯𝗹𝗶𝗰 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀
→ 494+ production-scale datasets
→ Learn cloud integration from day 1
→ https://lnkd.in/dAqnJHjv

𝟰. 𝗪𝗼𝗿𝗹𝗱 𝗕𝗮𝗻𝗸 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮
→ Global economic/social data
→ Perfect for real-world impact projects
→ https://lnkd.in/dYu-m2PK

𝟱. 𝗗𝗮𝘁𝗮.𝗴𝗼𝘃
→ 283K+ government datasets
→ 50+ formats, serious scale
→ https://data.gov/

𝟲. 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗦𝗲𝗮𝗿𝗰𝗵
→ Academic + research data
→ Find what others can't
→ https://lnkd.in/d-ziT_3z

𝟳. 𝗢𝗽𝗲𝗻𝗠𝗟
→ 5,400+ datasets with ML benchmarks
→ Compare your work to SOTA
→ https://www.openml.org/

NOTE:
One useful source of data would be:
Making use of LLM to build synthetic data

You definitely can practice on the synthetic data 
In fact it could be much closer to the real-world scenario!

Bookmark this to come back to it the next time.

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Three things remain consistent in 2026 
Python . PyTorch . GenAI

But GenAI ≠ just LLMs.

𝗧𝗵𝗶𝘀 𝗯𝗼𝗼𝗸 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘀𝗽𝗲𝗰𝘁𝗿𝘂𝗺.

Recently I reviewed the book:
Generative AI with Python and PyTorch (2nd Ed)
By Raghav Bali & Joseph Babcock (practitioners with AI patents & PhDs)

I particularly liked this book because:
Everyone talks about LLMs.
Few master image generation.
This teaches both at production scale.

𝗟𝗟𝗠 𝗠𝗮𝘀𝘁𝗲𝗿𝘆:
✅ RAG systems (LangChain, vector DBs)
✅ Fine-tuning without burning cash (LoRA, PEFT)
✅ Open-source deployment (Llama, Mistral)
✅ Prompt engineering that works

𝗜𝗺𝗮𝗴𝗲 𝗚𝗲𝗻 𝗠𝗮𝘀𝘁𝗲𝗿𝘆:
✅ Diffusion models (Stable Diffusion, DALL-E techniques)
✅ GANs for style transfer & deepfakes
✅ VAEs for optimization
✅ CLIP for vision-language models

Perfect if you're building GenAI systems 
that go beyond chat interfaces.

Book link - https://lnkd.in/dvjsfGbq
Github code - https://lnkd.in/dtpQ6MnH

Definitely great book for data scientists & ML engineers building GenAI who wants to learn building GenAI systems across Text as well as Image!

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This Open-source 122K GitHub stars app solves the hardest part of building AI apps.

And most AI engineers still don't know about it.

Dify gives you:

RAG Pipeline
→ Document ingestion to retrieval
→ PDF, PPT extraction out-of-box

Agent Framework
→ 50+ built-in tools
→ Google Search, DALL·E, Stable Diffusion

Visual Workflows
→ Build AI pipelines on a canvas
→ No more spaghetti orchestration code

LLMOps
→ Logs, monitoring, analytics
→ Improve prompts from production data

Must check it out.
122K developers can't be wrong.

Link in comments.

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Stop building RAG & AI Agents in Jupyter notebooks

Use this production-ready FastAPI base repo as your starting point.

This is Infrastructure-first approach!
🔹 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼 (builder-first):

1️⃣ FastAPI app 
→ clean src/ with routers / services / repositories / schemas
↳ Pydantic models, dependency injection, structured logging, config in .env

2️⃣ Database ready 
→ PostgreSQL + SQLAlchemy + Alembic
↳ migrations, seeds, environment-driven settings

3️⃣ Search & vectors 
→ OpenSearch (BM25 + vector) wired in
↳ ready for RAG retrieval out of the box

4️⃣ LLM hookup 
→ Ollama (local) endpoints you can swap later
↳ notebook included that explains setup end-to-end

5️⃣ DevX & ops 
→ pytest, ruff, uv, Docker/compose, Airflow (optional)
↳ reproducible installs, linting, pipelines, and a sane pyproject.toml

🔹 𝗤𝘂𝗶𝗰𝗸 𝘀𝘁𝗮𝗿𝘁
1️⃣ Clone & copy .env.example → .env
2️⃣ uv sync to install deps
3️⃣ docker compose up -d (FastAPI, Postgres, OpenSearch, Ollama)

🔹 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁
CI/CD + cloud infra in the next phases.

Link: https://lnkd.in/dCYbsY3c
(Also in the pinned comment if you need it later.)

Save this for your next project!

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Stop using Excel for tracking your experiments!
70% of ML projects fail because of bad tracking.
In 2026, every ML Engineer should know MLflow

It's the go-to tool for tracking and versioning models.

MLflow is now the default experimentation tool in Amazon SageMaker, Databricks, and beyond.

If you haven't explored MLflow yet - this is your chance 👇

MLflow for ML Development
A newly updated hands-on course by Manuel Gil covering everything you need:

1️⃣ Setting up MLflow for your local projects
2️⃣ Logging parameters, metrics, and models like a pro
3️⃣ Organizing and tracking experiments
4️⃣ Using the model registry to version your models
5️⃣ Tuning your models with MLflow
6️⃣ A full demo showing MLflow in action

✅ Complete video walkthrough
✅ Ready-to-use code examples

Video: https://lnkd.in/g-gnHUmd
Code: https://lnkd.in/gmexw3N8

💭 What are using for ML experiment tracking?
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With a fondness for mathematics, my academic and professional journey led me to data science, where I've had the opportunity to work on projects that have positively impacted various Businesses. My graduate studies in Germany allowed me to delve into areas like finance and Data Science, where I could apply Machine Learning to practical challenges.

In my toolset, you'll find skills in Python, SQL, MLOps, AWS, A/B testing, and Big Data technologies. I approach my daily tasks with a focus on collaboration, aiming to ensure data accuracy and meaningful insights.

I value continuous learning and open dialogue with peers in the industry. If you're interested, I'd welcome the chance to talk and learn from each other.

MLflow

Amazon SageMaker

Databricks

Machine Learning & MLOps Engineer

Carlos V.

If you are starting with AI engineering,
here are basics of RAG you should know.

Most people overcomplicate RAG.
Here's what actually matters:

𝗧𝗵𝗲 𝘁𝗿𝘂𝘁𝗵:
95% of production RAG systems
use the same basic components.

You don't need fancy architectures.
You need solid fundamentals.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻:
What RAG actually is:
↳ Retrieval = grounding in real data
↳ Generation = flexible, natural responses

That's it. Not magic. Just smart information retrieval.

𝗧𝗵𝗲 𝟱 𝗰𝗼𝗿𝗲 𝗽𝗶𝗲𝗰𝗲𝘀 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱:
1️⃣ Embedder (OpenAI works fine)
2️⃣ Retriever (vector search)
3️⃣ Reranker (optional but improves quality)
4️⃣ Prompt Constructor (assembles context)
5️⃣ Generator (your LLM)

𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿:
- Chunking: 512-1000 tokens with 20-100 token overlaps
- Hybrid Search: BM25 + vectors (better than vector alone)
- Metadata Filtering: source, date, type tags
- Prompt Engineering: "Use only provided context"
- Caching: speeds up + reduces cost

𝗦𝗸𝗶𝗽 𝘁𝗵𝗲𝘀𝗲 𝘂𝗻𝘁𝗶𝗹 𝗯𝗮𝘀𝗶𝗰𝘀 𝘄𝗼𝗿𝗸:
❌ Complex multi-agent systems
❌ Graph RAG architectures
❌ Custom fine-tuned embeddings
❌ Advanced routing logic

𝗧𝗼𝗼𝗹𝘀 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵:
↳ LangChain 
↳ Qdrant or Weaviate (vector DB)
↳ OpenAI embeddings (they work)
↳ RAGAS for evaluation

𝗛𝗼𝘄 𝘁𝗼 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲:
- Precision@k, Recall@k (retrieval quality)
- Faithfulness (uses provided context)
- Helpfulness (actually answers question)

That's the playbook.

Keep it simple.
Build on fundamentals.
Add complexity only when needed.

Most "advanced" RAG systems fail
because they skipped the basics.

Don't be that engineer.
Also remember the biggest component of many RAG systems is the pre-processing techniques e.g. OCR/multi-modal extractors.

What's your biggest RAG challenge?

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𝐌𝐨𝐬𝐭 𝐀𝐈/𝐌𝐋 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐥𝐞𝐚𝐫𝐧 𝐭𝐡𝐞𝐨𝐫𝐲.
4 end-to-end projects that will
actually make you production-ready:

1️⃣ 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐌𝐋 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞
↳ Build churn prediction for fintech/telecommunication platform
↳ Clean data with Pandas/Polars
↳ Train with LightGBM or CatBoost
↳ Track experiments with MLflow
↳ Serve with FastAPI + Docker on AWS
👉 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗠𝗟 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗶𝗻 𝗼𝗻𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁.

2️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐑𝐀𝐆 𝐂𝐡𝐚𝐭𝐛𝐨𝐭
↳ Ingest product support docs
↳ Chunk & embed the data
↳ Store in OpenSearch or pgvector
↳ Use Ollama or Jina LLMs (free)
↳ UI in Streamlit, monitor in Langfuse
👉 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗽𝗼𝘄𝗲𝗿𝘀 𝗿𝗲𝗮𝗹 𝗚𝗲𝗻𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀.
You can build this TODAY with our
free RAG course: https://lnkd.in/dXjBwX6Z

3️⃣ 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐞 𝐚𝐧 𝐋𝐋𝐌 𝐨𝐧 𝐍𝐢𝐜𝐡𝐞 𝐃𝐚𝐭𝐚
↳ Collect domain docs (medical, legal, etc.)
↳ Fine-tune with LoRA/DPO
↳ Evaluate for hallucination & accuracy
↳ Deploy with HuggingFace Inference
👉 𝗔𝗱𝗮𝗽𝘁 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗱𝗼𝗺𝗮𝗶𝗻.

4️⃣ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐟𝐨𝐫 𝐌𝐨𝐝𝐞𝐥 + 𝐃𝐚𝐭𝐚 𝐃𝐫𝐢𝐟𝐭
↳ Detect drift in CLV model post-deployment
↳ Track with Evidently or Arize
↳ Trigger retraining via Airflow
↳ Visualize metrics in Grafana
👉 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗼𝗻'𝘁 𝗲𝗻𝗱 𝗮𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝘁𝗵𝗲𝘆 𝘀𝘁𝗮𝗿𝘁.

Theory gets you interviews.
Production systems get you hired.

Which project are you building first?

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8 terms every AI/ML engineer should know

(and how they work together)

1️⃣ 𝗘𝗻𝗰𝗼𝗱𝗲𝗿
The foundation model that converts
text into vector embeddings

Think: Text → Numbers

"Machine learning" → [0.23, -0.45, 0.89...]
Similar concepts get similar numbers.

2️⃣ 𝗕𝗶-𝗘𝗻𝗰𝗼𝗱𝗲𝗿 (𝗗𝘂𝗮𝗹 𝗘𝗻𝗰𝗼𝗱𝗲𝗿)
Encodes query and documents separately

Encode 1M documents once.
Store them.
When query comes, encode only the query.
Compare against pre-computed embeddings.

𝗪𝗵𝘆 𝗶𝘁'𝘀 𝗳𝗮𝘀𝘁:
↳ Pre-compute doc embeddings once
↳ Only encode query at runtime
↳ Simple similarity comparison

𝗨𝘀𝗲 𝗳𝗼𝗿: Initial retrieval (Stage 1)

3️⃣ 𝗖𝗿𝗼𝘀𝘀-𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Processes query + document together

Reads both texts simultaneously.
Creates rich connections between every word.

Like having a conversation vs reading notes.

𝗪𝗵𝘆 𝗶𝘁'𝘀 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲:
↳ Full attention between texts
↳ Captures deep semantic relationships
↳ Learns exact relevance

Can't pre-compute. Must run per query-doc pair.
𝗨𝘀𝗲 𝗳𝗼𝗿: Final re-ranking (Stage 2)

4️⃣ 𝗥𝗲-𝗿𝗮𝗻𝗸𝗲𝗿
Any model that improves initial ranking

Retrieved 100 docs. Which 10 are truly best?
Re-ranker looks deeper at top candidates.

Think: First pass vs final review.

𝗧𝘆𝗽𝗲𝘀:
↳ Cross-encoder (most common)
↳ ColBERT (token-level matching)
↳ Cohere re-rank API
↳ Custom scoring models

5️⃣ 𝗗𝗲𝗻𝘀𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
Vector-based search using embeddings

Search by meaning, not exact words.

Query: "affordable housing"
Finds: "cheap apartments", "budget homes"
Vectors capture semantic similarity.

𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
↳ Neural encoders create embeddings
↳ Semantic similarity matching
↳ Finds meaning, not just keywords

𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Sentence Transformers, OpenAI embeddings

6️⃣ 𝗦𝗽𝗮𝗿𝘀𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
Traditional keyword-based search

Query: "machine learning tutorial"
Finds docs with those exact terms.

Counts word frequency. No neural networks.

𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
↳ BM25, TF-IDF algorithms
↳ Exact term matching
↳ Fast and interpretable

𝗨𝘀𝗲 𝗳𝗼𝗿: Hybrid search baseline

7️⃣ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵
Combining dense + sparse retrieval

Sparse catches exact matches.
Dense catches semantic matches.
Merge results intelligently.

𝗧𝗵𝗲 𝗺𝗮𝗴𝗶𝗰 𝗰𝗼𝗺𝗯𝗼:
↳ Sparse catches exact matches
↳ Dense catches semantic matches
↳ Weighted fusion of both scores

𝗥𝗲𝘀𝘂𝗹𝘁: Better than either alone

8️⃣ 𝗥𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗮𝗹 𝗥𝗮𝗻𝗸 𝗙𝘂𝘀𝗶𝗼𝗻 (𝗥𝗥𝗙)
Algorithm to merge multiple rankings

2 ranked lists. How to combine fairly?

RRF gives higher scores to docs
that rank high in both lists.

𝗨𝘀𝗲 𝗰𝗮𝘀𝗲:
Combining sparse + dense results into
one unified ranking

𝗙𝗼𝗿𝗺𝘂𝗹𝗮: Simple rank-based fusion
(no score normalization needed)

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Most people obsess over OpenAI, Anthropic.

Meanwhile, NVIDIA's resources are quietly dominating
both the hardware AND software sides of AI.

Here's what you're NVIDIA has done till now:

𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀
↳ DGX Spark: Run 100B param models on your desk
↳ DGX Station: 1T param models with 775GB memory
↳ Grace Blackwell: Petaflop-level AI performance locally

𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀
↳ NVFP4 format: 70% model compression, zero accuracy loss
↳ llama.cpp integration: 35% performance boost
↳ 8x faster than MacBook Pro M4 Max for video gen

𝗢𝗽𝗲𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
↳ Free playbooks for RAG, robotics, genomics
↳ Pre-configured CUDA-X libraries
↳ vLLM, SGLang, llama.cpp ready out-of-box

The amazing truth?
You can now run frontier AI models
on a deskside system that fits under your desk.

No cloud. 
No data center. 
No monthly bills.

Complete Sovereign! 
That's the future of AI!
Just local inference with production-grade performance.
Still not convinced?

Check out the free notebooks and setup guides:
https://lnkd.in/dViv8nUE

This is what AI infrastructure looks like when
hardware and software teams actually collaborate.
What's your take on local vs cloud AI workloads?

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This is unlocking people like me in another way.

I just built a complete new Jam With AI website in 20 minutes.

Walk with me and understand how the AI is evolving.
I used completely different tool: Atoms

As most AI tools: Pretty UI, no backend
Atoms: Frontend + Backend + Supabase + Stripe

𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝘁:
Website for JAM with AI:
→ Dark/light mode toggle
→ User authentication
→ Database (Supabase)
→ Payment processing (Stripe)

All from THREE prompts.

One thing I loved is the Race mode (check the video)
Instead of one AI/LLM building your app...
Atoms runs multiple AI teams in parallel.
→ Claude Sonnet
→ DeepSeek
→ Gemini Pro
→ Gemini Flash

They compete.
You pick the winner.
It settles the doubt in 20 mins!

The biggest win is 𝗧𝗵𝗲 𝗦𝘁𝗿𝗶𝗽𝗲 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻:

Most AI tools stop at the frontend:
"Here's your pretty website!"

But you can't make money with just UI.

Atoms Backend gives you:
→ Database for user data
→ Authentication for accounts
→ Stripe payments to accept money

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗮 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹.
𝗜𝘁'𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗲𝗮𝗺.

Try it: https://atoms.dev/

Who is building their website with me?
What would be your inspiration to build?

#VibeBusinessEra #BuiltwithAtoms

𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀.
𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗔𝗪𝗦 𝗦𝗮𝗴𝗲𝗠𝗮𝗸𝗲𝗿.
𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗠𝗟𝗳𝗹𝗼𝘄.
𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀.
𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗽𝗲𝗿𝘀𝗼𝗻'𝘀 𝗲𝗳𝗳𝗼𝗿𝘁.
𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝗠𝗟𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀.
𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻.

MLOps is a framework and a team effort, built on a set of 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀, 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀, 𝗮𝗻𝗱 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘀𝗵𝗶𝗳𝘁𝘀 to ensure:

✅ 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 
– ML systems that work consistently in production
→ True example: JPMorgan: 300+ models delivering $1.5B value annually

✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 
– Seamless handling of increased workloads
→ True example: Netflix: 3,000+ ML projects processing petabytes via Metaflow

✅ 𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆 
– Consistent results across experiments and deployments
→ True example: Stripe: p99 latency of 150ms with feature platform

✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 
– Tracking model performance and data drift
→ True example: Shopify: 216M daily predictions with real-time monitoring

✅ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 
– CI/CD pipelines, versioning, and deployment workflows
→ True example: Uber: 20,000+ features serving 6.8M peak predictions/second

MLOps is not just about tools - it's about building end-to-end operational excellence for ML systems.

The companies above?
They treat MLOps as a business capability, not a tech project.

𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗠𝗟𝗢𝗽𝘀 𝗯𝗹𝗼𝗴𝘀 𝘆𝗼𝘂 𝗺𝘂𝘀𝘁 𝘀𝗮𝘃𝗲:
1. Neptune.ai (Company case studies) - https://lnkd.in/dVwF2wyu
2. MLOps Community (Industry insights) - https://lnkd.in/dHXa5qR9
3. AWS ML Blog (Cloud MLOps) - https://lnkd.in/dKzwDzTU
4. Hopsworks (Feature store focus) - https://lnkd.in/dDt2DYYC

Any other interesting blogs you read for MLOps? 
Comment below 👇

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Uber

As a Data Scientist I used to obsess over models.

But the first step to production that matters? Containerization.

In the last 7 years, every single AI/ML project my team and I shipped to production had one thing common. 
➡️ It was Docker. 
Every. Single. Time.

From simple pipelines to complex AI agents, containerization made it possible to ship with confidence.

And it’s not going away anytime soon.
So if you’re a data scientist, ML engineer, or just getting started - I highly recommend learning Docker.

Here are 𝟯 𝗙𝗥𝗘𝗘 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 I recommend (weekend-friendly):
✅ Docker Containers for Beginners - https://lnkd.in/dyVmTsZa
✅ Docker Fundamentals - Cantrill - https://lnkd.in/dvGMpr3w
✅ YouTube Docker Course - https://lnkd.in/dnKai3jV

Master this, and you’ll be ahead of 80% of AI practitioners struggling with deploys 🚀

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