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

Do the Data

Founder

Deloitte

Data Analytics Manager

Novartis

Lead a team of data analysts and oversee the design and implementation of healthcare data analytics projects

Develop and maintain healthcare data management systems, ensuring data accuracy, completeness, and security

Develop and implement data analysis plans to support clinical and operational decision-making processes

Collaborate with stakeholders across the organization to identify and prioritize business intelligence needs, and ensure that data analytics projects align with the overall strategic goals of the organization
Analyze large datasets using statistical and data visualization tools to identify trends, patterns, and insights that can be used to drive business performance

Ensure compliance with data privacy and security regulations such as HIPAA, and work with legal and compliance teams to address any data-related issues

Develop and deliver presentations to communicate complex data analysis to non-technical stakeholders, including executive leadership, and make recommendations for process improvements based on data-driven insights

Stay up-to-date with the latest trends and technologies in healthcare data analytics and provide guidance on how the organization can leverage them to improve performance and patient outcomes

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

Python for Data Professionals: Why It’s the Preferred Choice

📍 Why is Python the go-to language for data roles?

Python isn’t just a programming language—it’s an essential tool for data professionals. Its simplicity, versatility, and vast ecosystem of libraries make it a top choice for handling complex data challenges with ease.

📍 Where does Python fit in the data ecosystem?

🔴 Data ingestion, cleaning, and transformation
🔴 Storage and orchestration
🔴 Visualization and reporting
🔴 Machine learning and model deployment

From building ETL pipelines to creating interactive dashboards, Python empowers data teams at every level.

📍 Why choose Python over Scala/Java?

While Scala and Java are excellent for large-scale data processing and system robustness, Python stands out for its flexibility. It’s a universal tool, adaptable across various data domains, making it the preferred choice for many professionals.
📍 Key data roles where Python is invaluable:
🔴 Data Analytics – Analyze data using Pandas, NumPy
🔴 Data Visualization – Create stunning visuals with Seaborn, Matplotlib, Plotly
🔴 Machine Learning – Build models with Scikit-learn, TensorFlow, Keras
🔴 Web Scraping – Extract data using Scrapy, BeautifulSoup, Selenium
🔴 Data Engineering – Implement pipelines with Spark, Kafka, Flink
🔴 Databases – Work with SQLite, PostgreSQL, MongoDB, MySQL

📍 Top free platforms to upskill in Python:
 - realpython.com
- pythonprogramming.net
- datacamp.com (free basic courses)
- w3schools.com/python
- https://lnkd.in/gCDhJvA2
- https://lnkd.in/grqYWfGW

 📍 Follow these Python experts for insights: 🐍

Matt Harrison, Khuyen Tran, Guido van Rossum, Dan Bader

Python remains an essential skill for data professionals—whether you're analyzing, engineering, or visualizing data, it has the tools to get the job done!

CC:Brij Kishore Pandey

Ravena  O

Ever wondered how companies actually build AI systems powered by LLMs?

It’s not just about calling an API and hoping for magic.

There’s an entire tech stack behind the scenes — and once you understand it, you’ll see how everything connects.

Let’s break it down step by step 👇

🟡 Step 1: The Brain (LLMs)
 Think of GPT, Gemini, or Claude as the intelligence layer.
 They understand, reason, and respond — but they need context, data, and direction.

🟡 Step 2: The Connectors (Frameworks)
 Frameworks like LangChain or LlamaIndex act as plumbing between your model and your tools.
 They let you combine the LLM with data sources, APIs, and logic — no need to reinvent the wheel.

🟡 Step 3: The Memory (Vector Databases)
 LLMs forget fast.
 That’s where databases like Pinecone, Weaviate, and Chroma come in — they store “embeddings” (a fancy word for text turned into numbers).
 It’s like giving your AI Google Search for your own data.

🟡 Step 4: The Fuel (Data Extraction)
 No data, no intelligence.
 Tools like Crawl4AI and FireCrawl pull information from PDFs, websites, and documents — then clean it so your AI can actually use it.

🟡 Step 5: The Open Source Edge
 Don’t want to rely on expensive APIs?
 Use open models from Hugging Face or run local ones via Ollama.
 Freedom + privacy + control = ✅

🟡 Step 6: The Language of Numbers (Embeddings)
 Before your text hits the vector DB, it must be converted into numerical form.
 That’s where OpenAI Embeddings or SBERT come in — enabling true semantic understanding.

🟡 Step 7: The Quality Gate (Evaluation)
Now test it.
Use tools like Giskard or Trulens to check for accuracy, hallucinations, and relevance.
Because every smart system needs smarter feedback loops.

💥 In short:
 1️⃣ Pick your LLM
 2️⃣ Connect it via a framework
 3️⃣ Feed it structured data
 4️⃣ Store it in a vector DB
 5️⃣ Add embeddings for meaning
 6️⃣ Use open tools for flexibility
 7️⃣ Evaluate relentlessly

This blueprint can help your team go from idea to intelligent system.

 🔁 Repost if you want your network to build smarter with AI.

CC: Rahul Agarwal

Ever wondered how Agentic AI actually works behind the scenes?

Here’s a simple breakdown of how an AI agent transforms raw input → into intelligent output.

 Perfect for anyone curious about how autonomous AI systems really think. 

🟠 Input Sources
 Agentic AI starts by collecting data from multiple channels — knowledge bases, user queries, APIs, logs, and web data.

🟠  AI Processing
Once data is gathered, the agent gets to work — analyzing the query, reasoning through context, retrieving memory, planning tasks, selecting tools, and managing the entire flow.

🟠 3. Action Layer
 With a clear understanding of context, the agent performs real-world actions — making decisions, executing tasks, fixing errors, collaborating with other agents, and even scheduling future steps.

🟠 4. Output
 Finally, it delivers a response that’s accurate, relevant, and context-aware — the hallmark of truly autonomous intelligence.
That’s what sets Agentic AI apart from traditional chatbots or rule-based systems.

 💾 Save this post for your next deep dive into AI systems!

CC: Prem Natarajan

We made these 4 books mandatory for every AI Engineer on our team.

Because building AI Agents isn’t about chaining prompts — it’s about mastering design, orchestration, and deployment at scale.

Here’s what’s worth your time 👇

📘 1. AI Agents: The Definitive Guide — Nicole Koenigstein

Goes deep into design, deployment, and evaluation.
If you’re tired of “just chain some prompts” advice — start here.
It’s the systems thinking most teams are missing.
https://lnkd.in/dJ9wFNMD

🤖 2. Building Applications with AI Agents — Michael Albada

Multi-agent systems, proper orchestration, the works.
Finally — a book on making agents collaborate without chaos.
https://lnkd.in/dSs8srk5

🎨 3. An Illustrated Guide to AI Agents (Coming soon)

Visual intuition for reasoning, multimodal LLMs, and diffusion models.
If you learn best by seeing, this is your go-to.
https://lnkd.in/deSEikJH

⚙️ 4. AI Agents with MCP — Kyle Stratis

Model Context Protocol for clients, services, and end-to-end agents.
The protocol layer most people skip — but shouldn’t.
https://lnkd.in/dR22bEiZ

All early releases. All raw. All genuinely useful.

💭 Which one are you starting with?

No alternative text description for this image

freeCodeCamp  recently released a comprehensive AI Engineering Roadmap — and it’s surprising how many people missed it.

It’s not just another tutorial list.
It’s a reality check for anyone serious about building a career in AI.

Here are some key takeaways worth reflecting on 👇


🔴 1. Math is non-negotiable
Linear algebra, calculus, and probability form the backbone of every AI system.
If you can’t understand gradients, optimization, and loss functions — you’ll struggle to build anything meaningful.

🔴 2. Python remains the foundation
From TensorFlow and PyTorch to NumPy and Pandas — this ecosystem drives modern AI.
Fluency in Python isn’t optional; it’s expected.

🔴 3. LLMs aren’t everything
While everyone is chasing ChatGPT replicas, real impact (and opportunity) lies in building domain-specific models that solve practical business problems.

🔴 4. Depth beats credentials
Building and deploying CNNs, RNNs, and LSTMs will teach you far more than any certificate ever could.

🔴 5. Data work is underrated
Data cleaning, transformation, and validation consume most of an AI engineer’s time — and mastering these makes you truly valuable.

🔴 6. Prompt engineering isn’t the destination
It’s a useful skill — but true AI engineers design systems, not just prompts.

🔴 7. Deployment is the differentiator
A model sitting in a Jupyter notebook has no value.
Productionization is where engineering begins.

🔴 8. Generative AI goes beyond LLMs
Understanding GANs, VAEs, and foundational architectures separates professionals from hobbyists.

🔴 9. System design matters
AI solutions must scale. Knowing Kubernetes, Docker, and distributed systems is critical for real-world impact.

🔴 10. Ethics and bias awareness are essential
As AI adoption accelerates, responsible design and fairness aren’t optional — they’re requirements for trust and compliance.



In short:
The roadmap reminds us that AI engineering isn’t just about chasing trends — it’s about mastering fundamentals, building production-ready systems, and thinking responsibly about the technology create

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Ravena O

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Ravena O

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