Voxel51

• Explore, tinker with, and push the boundaries of the latest AI and machine learning technologies to showcase what's possible with open-source tools and frameworks

• Build inspiring prototypes, proof-of-concepts, and example applications that demonstrate creative and impactful uses of AI to the developer community

• Contribute meaningfully to the open source ecosystem by creating and maintaining libraries, tools, datasets, tutorials and guides that enable developers to more easily leverage AI capabilities

• Curate, clean, annotate and publish unique multimodal datasets spanning text, images, audio, video, 3D and other emerging data types to facilitate novel machine learning research and applications

• Create engaging technical blog posts, social media content, videos, and presentations that educate and inspire developers and data scientists to build with AI

•  Collaborate openly with our internal teams and the broader AI/ML community to cross-pollinate ideas, share knowledge, and collectively advance the state-of-the-art

• Evangelize our technology and vision by representing the company at hackathons, conferences, meetups, universities, and other developer-focused events

•  Influence the product roadmap by providing input on the developer experience, uncovering opportunities for new features and improvements based on community feedback and your work

• Continuously learn, experiment with, and provide feedback on beta versions of our latest models and capabilities to help shape the future of our platform

Hacker-in-Residence

LinkedIn

1) Introductory computer vision course for LinkedIn Learning

2) Prompt Engineering with LangChain (releasing March 2024)

3) RetrievalAugemnted Generation with LlamaIndex (releasing Q2 2024)

4) Fine-tuning LLMs with Hugging Face TRL (est release Q3 2024)

5) Text Generation with Hugging Face Transformers (est release Q3 2024)

LinkedIn Learning Instructor

The Artists of Data Science

The ONLY personal growth and development podcast for Data Scientists

Host

Deci AI

Developer Relations Manager

Pachyderm Inc.

Senior Developer Advocate

Comet

ML Developer Advocate

Data Science Dream Job

Principal Data Science Educator

Price Industries

Lead Data Scientist

Bold Commerce

Senior Data Scientist

Emergent BioSolutions

Biostatistician

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 🥑

sam3 is seriously a step change improvement over sam2

the model is seriously good, glad they brought back text prompts. the automatic segmentation isn't as good as i hoped it to be...but it still works quite well. 

i also got it to output image embeddings and that was cool to visualize in the app

learn more here: https://lnkd.in/gzqJzj6G

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

qwen3vl is literally the best model i've used for video usecases. blows my mind

hosting a virtual event tomorrow about document ai

you may or may not find it interesting, but i guarantee you'll learn something

join me here: https://lnkd.in/dFXPimVc

sony ai released a pretty cool dataset called the fairness human centric image benchmark, super high quality labels

instructions for downloading and parsing the dataset into fiftyone format can be found here: https://lnkd.in/g_VwxJx4

physical AI is top of mind as the next frontier for deep learning. but it'll only succeed if you have good datasets

language models scaled with web-scale text. 

vision models got ImageNet. 

now Voxel51 and NVIDIA are trying to do the same thing for physical AI 

autonomous vehicles, humanoid robots, anything that moves around atoms.

building these systems means millions of hours of testing in high-fidelity sims before you can actually deploy anything. 

petabytes of multimodal sensor data from various modalities

 LiDAR, cameras, radar, IMUs. 

but over 50% of simulations end up completely unusable because the input data is bad.

even small calibration or timing errors between sensors cascade through your neural reconstructions. 

you waste weeks and burn through millions in compute costs before you realize what went wrong.

the new FiftyOne Physical AI Workbench. it integrates with NVIDIA Omniverse and NVIDIA Cosmos and basically sits at the start of your sim pipeline to validate, clean up, and prep your multimodal data before it hits neural reconstruction and synthetic data generation.

what it does:

- automatically audits physical AI inputs across 75+ critical checkpoints

- transforms raw sensor streams into structured, searchable datasets

- triggers accurate neural reconstructions and generates synthetic scene variations

this means fewer wasted compute hours. 

iteration loops go from weeks to hours. 

you build safer, more reliable systems on data you can actually trust.

read more about what we've been building: https://lnkd.in/gC6_zn-X

i recently integrated 4 ocr models into fiftyone as remote zoo models these handle text extraction and document parsing. 

all available as remote zoo sources, you can get started with a few lines of code

different approaches for different needs:

1. mineru-2.5

1.2b params, two-stage strategy: global layout on downsampled image, then fine-grained recognition on native-resolution crops. 

handles headers, footers, lists, code blocks. strong on complex math formulas (mixed chinese-english) and tables (rotated, borderless, partial-border).

good for: documents with complex layouts and mathematical content

https://lnkd.in/dSwimdvp

deepseek-ocr

dual-encoder (sam + clip) for "contextual optical compression." 

outputs structured markdown with bounding boxes. has five resolution modes (tiny/small/base/large/gundam). gundam mode is the default - uses multi-view processing (1024×1024 global + 640×640 patches for details). 

supports custom prompts for specific extraction tasks.

good for: complex pdfs and multi-column layouts where you need structured output

https://lnkd.in/dhJn2h-v

olmocr-2

built on qwen2.5-vl, 7b params. outputs markdown with yaml front matter containing metadata (language, rotation, table/diagram detection). 

converts equations to latex, tables to html. labels figures with markdown syntax. reads documents like a human would.

good for: academic papers and technical documents with equations and structured data

https://lnkd.in/dNdTQYKg

kosmos-2.5

microsoft's 1.37b param multimodal model. two modes: ocr (text with bounding boxes) or markdown generation. automatically optimizes hardware usage (bfloat16 for ampere+, float16 for older gpus, float32 for cpu). handles diverse document types including handwritten text.

good for: general-purpose ocr when you need either coordinates or clean markdown

https://lnkd.in/dCTfGfV8


two modes typical across these models: detection (bounding boxes) and extraction (text output)

i also built/revamped the caption viewer plugin for better text visualization in the app: 

https://lnkd.in/dBebWdga

i've also got two events poppin off for document visual ai:

- nov 6 (tomorrow) with a stellar line up of speakers: https://lnkd.in/gkBEY4_r

- a deep dive into document visual ai with just me: 

https://lnkd.in/dFXPimVc

Open article: Document Visual AI with FiftyOne - November 14, 2025 by voxel51.com

Document Visual AI with FiftyOne - November 14, 2025

hype for this new integration, and proud of the team for shipping it at lightning speed. big ups to Daniel and AdonAI for goin beast mode on this one

🚀 Introducing Physical AI Workbench — integrated with NVIDIA Omniverse NuRec and NVIDIA Cosmos to solve the biggest bottleneck in physical AI.  

More than half of all physical AI simulations fail — not from bad models, but from bad input data. A timestamp off by milliseconds or a calibration drift of a few degrees can cascade through your neural reconstructions, leaving you with unusable results after weeks and millions in wasted compute. 

FiftyOne Physical AI Workbench sits at the start of your simulation pipeline and provides turnkey access to NVIDIA technology to ensure every simulation run begins with trustworthy data: 

✅ Audits 75+ critical checkpoints to ensure every reconstruction starts with validated inputs 
🎯 Enriches raw sensor streams into structured, searchable datasets 
🔗 Integrates directly with NVIDIA Omniverse and NVIDIA Cosmos for high-fidelity reconstruction and synthetic data generation. 

The result: an end-to-end workflow that prevents downstream failures — cutting iteration cycles from weeks to hours and eliminating GPU waste on failed reconstructions. We’re excited to see this workflow power autonomous vehicles NVIDIA DRIVE and robotics NVIDIA Robotics development teams move faster with higher accuracy. 

🔗 Read the full technical breakdown: https://lnkd.in/gTy5-TNF

Non-Brand Data

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Founder

Berdata

Berdata provides the latest information about data science and related fields with up-to-date blogs and educational material in Bahasa, Indonesia.

We also provide services to fulfill your data science and machine learning model needs, including:

- Data Analysis and Visualization
- Predictive Modeling and Machine Learning
- Business Intelligence (BI) Solutions
- Generative Artificial Intelligence and Deep Learning Solution Development
- Data Strategy and Consulting
- Optimization and Automation
- Training and Workshops

Arif Analytics

As the Lead Data Scientist and Startup co-founder, I oversee the data science team and steer the direction of the AI product. I will also collaborate with the other departments to ensure the AI product is on the right track.

My responsibilities including:
• Led the development of Data Science Dashboard features, including Data Visualization, Speech-to-Text, and Statistical Analysis, and enhanced the platform with LLM-powered Retrieval-Augmented Generation (RAG) and Agent capabilities
•Produce Product Requirements Document (PRD) and ensure the direction of AI product following the company vision.
•Managing the AI product usage and the cost incurred related to the AI activity.
•Collaborated with the team members to achieve target results and meet deadlines. Validate the timeline with the developer teams.

Lead Data Scientist (Generative AI/ LLM)

Stealth Startup

As an AI Engineer, I specialize in developing advanced AI products that leverage both open-source and closed-source LLM services. The product focuses on validating the business and connecting with the investor with AI.

My responsibilities including:
• Develop Financial-AI powered application with both open-source and closed-source LLM services.
• Implement AI models’ architecture and ensure the AI product met the company vision.
• Ensure AI models seamlessly integrated with the existing system.
• Translate the business requirement into necessary solution required from the application.

AI Engineer (Generative AI/ LLM)

KDnuggets

Contributing Writer

Allianz

•Generate Data Science end-to-end working framework for the company and implement MLOps design within the framework. End-to-end means from understanding the business problem to the model monitoring.

•Successfully implemented and supervised various Machine Learning model to improve the business quality, including:

    o Successfully implemented and supervised Propensity-to-Buy Machine Learning models in several business lines to meet the business target and achieved close to 150% of the Annual Target.
   o Develop a customer segmentation model and analyze the segment data to provide insight into actions suitable for each segment. The action insight is used to support the Propensity-to-Buy project.
   o Successfully implemented and supervised Churn Prediction Machine Learning models to avoid customer loss. Help decrease churn rate by 3%.
   o Technical Project Leader for Fraud Detection Model Project in 2024 that help improve claim fraud business pipeline.

•Leading teams of data scientist junior for the Machine Learning Project and educate the teams to be more independent

•Educate the company regarding the benefit of Data Science and Machine Learning to bridge the business and technical requirement

•Develop Microstrategy Business Intelligence Dashboard for Business User based on the Machine Learning project

•Implement the ML Cloud-Based Services AWS within the company, specifically for end-to-end data science use cases (In progress)

Data Scientist Assistant Manager (ML / LLM/ MLOps)

- Working day-to-day on the data project within the business to increase the business values of the company using the power of the data
- Harnessing the power of Machine Learning within the company department depends on the business user requirements. 
- Educate people for the AI and Machine Learning benefits to the Business user

Data Scientist Supervisor

Purwadhika Startup School

•	Successfully conducting a classroom session for Data Science Course with participants both from a professional industrial background (Banking, Finance, HR, Math, Marketing, etc.) and Fresh Graduate. 

•	Responsible for creating the Data Science teaching material and successfully implemented this material in the teaching session. Material including Python Programming Fundamental, SQL Fundamental, Data Science Analysis Methodology (Focus in Statistical Analysis and Visualization Analysis), and Machine Learning for Data Science. 
 
•	Responsible to supervise the social media analysis project. Currently in progress of the sentiment analysis project. Future project plan including customer segmentation analysis and forecasting.

Head of Data Science

Potentialpark

•	Auditing career website from a various company and provide feedback to the team leader
•	Gained experience in detail work and teamwork

Auditor

Department of Cell and Molecular Biology, BMC, Uppsala University

•	Assisting ongoing research by planning research plan, documenting research progress and analysing the research data
•	Establishing a Standard Operational Procedure for one of the research at the laboratory

Summer Research Assistant

Level up Your ML Game ❤️️| Writing a Lot | Data Mentor

🔹 I turn data into ROI for businesses by designing AI systems that solve high-impact problems—from fraud detection to customer retention. With 6+ years leading enterprise data science teams (like Allianz) and deploying LLM-powered tools, I bridge the gap between technical teams and business stakeholders.

🔹 I've written 500+ articles for platforms like KDNuggets, Machinelearningmastery, Towards Data Science, and Non-Brand Data (2M+ monthly readers), breaking down complex AI/ML concepts into actionable insights. My content isn't just theory—it's battle-tested from projects like:
✅ Propensity models that drove 150% of Allianz’s annual revenue targets
✅ Fraud detection systems reducing losses by 7-figure margins
✅ LLM chatbots with RAG that cut customer support costs by 30% 

🔹 Now, I help professionals and companies:

👉Consulting: Machine Learning Pipeline, LLM product design, and AI strategy.
👉Mentorship: Transition into data science careers via 1:1 coaching (100+ mentored since 2019).
👉Content: Partner with brands for technical articles, workshops, or whitepapers.

Let’s collaborate if you need:

📌A pragmatic AI consultant who ships code, not just slides.
📌A technical writer to simplify AI/ML for your audience.
📌A mentor to fast-track your data science career.

📩 DM me for consulting inquiries, mentorship slots, or content partnerships.

taplio

The Battle

Harpreet Sahota 🥑

Cornellius Yudha Wijaya

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taplio

The Battle

Harpreet Sahota 🥑

Cornellius Yudha Wijaya

Followers

Posts

Engagements

Likes

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

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