I create content about deep learning | The DevRel and Deep Learning Dude

Hacking on LLMs, curating multimodal datasets, and writing a book about RAG.

Harpreet Sahota 🥑

i've literally been waiting for years to have an OPEN SOURCE model like qwen3-vl-embedding

i tested its multimodal retrieval capabilities on a corpus of 412 short video clips and the results literally blew my mind

here are the queries i tested:

1. a cartoon guy drinks merlot wine

i like this query because we see how it can retrieve based on semantics (a cartoon), text (the label merlot), and temporal action in the video (the cartoon guy drinks the wine mid-way through the video)

2. garfield runs out of the door

semantics are awesome here, the model knows i'm talking about the cartoon character...

3. a woman in a blue dress sleeping on a red bench

the skeptic in you might think that it's retrieving just based on the red color block in the 2/3 of the video...but notice the specific part of the query "a woman in a blue dress..." and this is only shown in 3s out of the full 10s

this is such a huge release 

and it's gonna open up SO much more for multimodal video retrieval this year

on my wishlist is natural language search of pcd datasets, who gonna ship that?

you can hack around with the model using the resources below

check out the docs: here: https://lnkd.in/gh7yfBNP

and the quickstart nbhere:  https://lnkd.in/gbinsx7p

🤖 Hacker-in-Residence @ Voxel51| 👨🏽‍💻 AI/ML Engineer | 👷🏽‍♀️ Technical Developer Advocate | Learn. Do. Write. Teach. Repeat.

for those who missed it, or were there in-person and know the trouble i was facing 😆, my ODSC webinar is now live on youtube!

in this session i discuss visual ai techniques for automotive damage detection. 

i used the cardd dataset, which is a public dataset built specifically for vehicle damage analysis and demonstrated how fiftyone helps you explore, visualize, and build models to detect dents, scratches, cracks, glass shatter, tire flats, and broken lamps.

i feel like its a super practical workshop with some insightful workflows for visual AI in automotive settings, but as the creator i might be biased.

only one way to find out for yourself, watch the webinar!

https://lnkd.in/ewfz5vZD

Open video: Mastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques by Harpreet Sahota on youtube.com

Mastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques by Harpreet Sahota

sometimes i look back at the shit i get done and i genuinely impress myself

how teams really build Visual AI in the wild? what's the he messy reality?  

i don't know...and maybe its because nobody’s asking practitioners and compiling their answers so we can find out the landscape actually looks like. 

so, we at Voxel51 are kickin off the...

wait for it...

 State of Visual AI survey...vey...vey

for those out ther building visual AI systems i want your hot takes

what’s the hardest part about making visual ai a reality? 

let us know in this survey

it'll take you 30 minutes and is a much better use of time than baby sitting your runs and staring at your loss curves

i can't offer much beyond my heartiest thanks, but you will get early access to the full report with benchmarks, common pain points, and where you stand within the landscape. 

here's a link: https://lnkd.in/gnsADACy

MedGemma 1.5 supports detection, but for best results, you'll need to fine-tune. 

there's a kaggle competition using the model, i created a starter notebook to give you a jump start on how to fine-tune it for detection

checkout the starter notebook and additonal resources 👇🏼

cc Omar Sanseviero Megan Risdal Logan Kilpatrick

looking forward to the next meetup in boston!

Join our in-person meetup at the Microsoft Research Lab at the Massachusetts Institute of Technology in Boston, Massachusetts on Feb 18 to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Register here: https://lnkd.in/gi4kpSMB

Talks will include:

• SNAP: Towards Segmenting Anything in Any Point Cloud - Hanhui Wang at Northeastern University

• Culturally Adaptive AI - Anku R. at Massachusetts Institute of Technology

• Data Foundations for Vision-Language-Action Models - Harpreet Sahota 🥑 at Voxel51

• Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models - Sharut G. at Massachusetts Institute of Technology

• Neural Radiance Fields for Image Verification - Sage Simhon at Pasteur Labs & ISI

#computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

Syed Abdul Sami Rizvi, Matthew Ebisu and 118 other attendees

View event: Feb 18 - Boston AI, ML and Computer Vision Meetup

Feb 18 - Boston AI, ML and Computer Vision Meetup

in the asu area and wanna get started with open source contributions? then don't miss this hack

On March 21, 2026, we’re hosting Agents World: Visual AI Hackathon @Arizona State University, together with Pavan Turaga 🚀 

This is a hands-on day: meet people, build with AI, eat pizza, and leave with something you created after a short workshop and a full day of hacking. 
You’ll work with the FiftyOne ecosystem and real models used in practice, like Qwen, Gemini Vision, Sharp, and more. 

If you’re an ASU student who wants to build, learn by doing, and work with real Visual AI tools, this is for you.

Registration is open:
https://lnkd.in/gBCHW5Fg

Hope to see you at ASU. I’ll be there with the amazing Voxel51 team:  Sid Mehta, Ritchie Martori, Jimmy Guerrero ! 🙌

Inspired by the amazing Hugging Face’s 2025 AI Timeline, we created one for the Fiftyone community. The FiftyOne Community Development Timeline highlights more than 131 contributions from 2025, including datasets, models, plugins, and integrations.

You can filter by type or modality and jump straight to each integration.
https://lnkd.in/ev2kpCGN

Big thanks to the entire Voxel51 community for building all of this together.
Harpreet Sahota 🥑, Daniel Gural, Paula Ramos, PhD, Jimmy Guerrero, Derek Van de Streek, Nicholas Lotz 🚀

Gonna be a great series of talks, lots of technical depth!

Chief Community Officer @ Voxel51 | Startup Advisor

Join us on Feb 18 for the Boston AI, ML and Computer Vision Meetup! Pre-registration is mandatory to clear building security - https://lnkd.in/gHtBuH49

Talks will include:

* SNAP: Towards Segmenting Anything in Any Point Cloud - Hanhui Wang at Northeastern University

* Culturally Adaptive AI - Anku R. at MIT

* Data Foundations for Vision-Language-Action Models - Harpreet Sahota 🥑 at Voxel51

* Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models - Sharut G. at MIT

* Neural Radiance Fields for Image Verification - Sage Simhon at Pasteur Labs & ISI

***********

Want to build better computer vision models? FiftyOne is an open source toolkit from Voxel51 (our Meetup sponsor) that helps you curate datasets, evaluate model performance, visualize embeddings, catch annotation errors, and eliminate duplicate images—all in one place.

“pip install fiftyone” is all it takes to get started - https://docs.voxel51.com/

#computervision #ai #artificialintelligence #machinevision
#machinelearning #datascience #physicalai

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Ravena O

Is your “Agentic AI” actually intelligent… or just automated chaos?

Most people reduce Agentic AI to LLMs wired to a few tools.
That’s a comforting myth — and a costly one.

Real-world agent systems don’t fail because of weak models.
They fail because the system around the model collapses.

Agentic AI is a stack, not a feature.
And every layer has a job to do 👇

1️⃣ AI & ML — Decision Backbone
Turning signals into predictions using supervised, unsupervised, and reinforcement learning.

2️⃣ Deep Learning — Pattern Engine
Transformers and neural networks that learn representations at scale.

3️⃣ GenAI — Expression Layer
🟡 LLMs generate content
🟡 RAG injects context
🟡 Multimodal models handle text, image, audio, video

4️⃣ AI Agents — Action Layer
🟡 Planning & reasoning (ReAct, CoT, ToT)
🟡 Tool usage & orchestration
🟡 State + context management
🟡 Human-in-the-loop control

5️⃣ Agentic AI Systems — Control Plane
🟡 Governance & guardrails
🟡 Observability & tracing (why did it act?)
🟡 Memory policies & retention
🟡 Rollback & failure recovery
🟡 Cost & resource management
🟡 Multi-agent coordination

Hard truth:
Smart prompts don’t create reliable systems. Architecture does.

If you can’t explain an action, reverse a decision, audit memory, or contain failure —
you didn’t build autonomy. You built fragile automation.

❌ Stop asking: “Which LLM should we use?”
✅ Start asking: “What happens when this agent fails at 2 AM?”

That question separates demos from production.

Ravena  O

Still confused between LLM, RAG, AI Agents, and MCP?
You’re not alone — most teams mix these up and design the wrong systems.

This visual breaks it down clearly ⬇️
Here’s the mental model that actually works:

🟡 LLM (Large Language Model)
• Pure reasoning + text generation
• No external knowledge beyond training
• Best for summarisation, Q&A, ideation

🟡 RAG (Retrieval-Augmented Generation)
• LLM + external context (docs, DBs, PDFs)
• Reduces hallucinations
• Ideal for enterprise knowledge, internal search

🟡 AI Agents
• LLM + planning + memory + tools
• Can take actions, not just respond
• Used for workflows, automation, copilots

🟡 MCP (Model Context Protocol)
• Standard way for models to talk to tools & data
• One unified interface → many systems (GitHub, Slack, files)
• Critical for scalable, tool-driven agent ecosystems

👉 Think of it like this:
• LLM = Brain
• RAG = Brain + Books
• Agent = Brain + Hands
• MCP = Nervous System connecting everything

Why this matters

• Most AI failures are architecture mistakes, not model issues
• Agents without MCP don’t scale
• RAG without governance breaks fast

Learn more (high-quality resources):

• RAG explained: https://lnkd.in/dUfV33DE
• AI Agents overview: https://lnkd.in/dE9A5G29
• MCP (Model Context Protocol): https://lnkd.in/dNMmQnSf
• Agent design patterns: https://lnkd.in/df4Yeuug

💡 If you’re building GenAI systems in 2026, this stack isn’t optional — it’s foundational.

What are you building right now: RAG, Agents, or full MCP-based systems? 👇

Small Language Models are the future of AI Agents
—but only if you know how to use them correctly.

Gartner predicts that by 2027, organizations will use small, task-specific AI models 3× more than general-purpose LLMs.

NVIDIA confirms it clearly:
“Small Language Models are the future of AI Agents.”

So what actually makes SLMs so powerful for agentic systems?

I put together a simple handbook to make this easy to understand.

Here’s a quick breakdown.


What are SLMs?

Transformer-based neural networks with fewer parameters (millions to low billions) that prioritize efficiency over broad generalization.

They deliver:
🟠 Faster inference
🟠 Lower memory usage
🟠 Easier deployment in production

🟠 How are SLMs created?

Using three core techniques:

Quantization – Reduces bit precision (32-bit → 8-bit) while preserving accuracy
🟠 Pruning – Removes less important parameters to shrink model size
🟠 Distillation – Transfers knowledge from large “teacher” models to compact “student” models


🟠 4 strategies to maximize SLMs in AI agents

🟠 Intelligent Routing – Simple tasks go to SLMs, complex ones to LLMs
🟠 Pipeline Collaboration – SLMs preprocess, LLMs refine outputs
🟠 Parallel Verification – SLMs draft quickly while LLMs verify
🟠 Conditional Activation – Escalate to LLMs only when confidence is low


🟠 Real-world implementations

🟠 Uber – SLMs handle query preprocessing and answer validation in Agentic RAG
🟠 OpenAI – SLMs power guardrails, relevance checks, and intent classification
🟠 Microsoft – SLMs improve cloud supply chain fulfillment accuracy

🟠 Why enterprises are adopting SLMs

🟠 Privacy-first – On-prem deployment for healthcare triage and legal reviews
🟠 Cost-efficient – 50,000+ receipts/day processed at ~1% of LLM cost
🟠 Hyper-specialized – Fine-tuned models for internal coding and support routing

🟠 Case studies

🟠 Uber: https://lnkd.in/geNJNhvK
🟠 OpenAI: https://lnkd.in/gD5-v58d
🟠 Microsoft: https://lnkd.in/gZ5hGuNy

If this post helped you rethink Agentic AI,
🟠 Like
🟠 Share
🟠 Or comment how you’re using SLMs in production

CC: Rakesh Goel

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