Nebius’ Post

🔎 Data annotation turns datasets from opaque files into valuable, reusable research assets - it’s one of the key enablers of Open Science and the FAIR principles. In our new article - 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻 – 𝗙𝗿𝗼𝗺 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝘁𝗼 𝗠𝗲𝗮𝗻𝗶𝗻𝗴 – 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗢𝗦𝗖𝗔𝗥𝗦 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲 𝘁𝗼 𝗶𝘁 -, we explore how the five Science Clusters (ENVRI community, ESCAPE, LS-RI, PaNOSC, SSHOC - Social Sciences and Humanities Open Cloud) are addressing the challenges of metadata, vocabularies, and annotation tools, and how OSCARS and its funded projects are fostering cross-cluster collaboration to strengthen FAIR data annotation practices in research. 📖 Read the article here: https://lnkd.in/dX7fU7-Y #OpenScience #FAIRdata #EOSC #OSCARS #Metadata #ScienceClusters

The newly released AI3 dataset presents a significant advancement in drug discovery, offering molecular dynamics trajectories for over 16,000 protein-ligand complexes. This resource, harnessed through AWS, enables researchers to leverage AI in addressing pivotal challenges in therapeutic development. By democratizing access to this comprehensive data, we're fostering innovation and enhancing the potential to transform health outcomes globally. Explore the implications of this data for machine learning applications in structural biology and drug design.

In machine learning, the double moon problem is a classic example: two interleaving half-moon shapes used to test how algorithms handle non-linear decision boundaries. It’s a simple and visual tool for exploring classification! I have just recently published a Public Job Template that anyone can use to: 🔹 Run and rerun jobs that test different models 🔹 Compare how decision boundaries shift with parameters 🔹 Validate results reproducibly Curious to try it yourself? ➡️ https://lnkd.in/dawEUwMf You will see that the Jobs framework at DECTRIS CLOUD is a sandbox for automation in ML workflows. For us, this is a small but meaningful demonstration of how reproducibility and transparency can transform research. Let me know what you think 😊

Data analysis shouldn’t drain your budget, yet most labs spend more on managing pipelines than on generating insights. With Basepair, analysis happens directly in your own cloud. That means: ❌ No duplicate data storage fees ❌ No wasted spend on underused infrastructure ✅ Predictable costs that scale with your needs ↪ For hospitals, this means controlled budgets without sacrificing compliance. ↪ For clinical teams, it means faster results without adding headcount. ↪ For full-service providers, it means serving more clients at a lower cost per sample. Smarter bioinformatics isn’t just faster—it’s more cost-effective. What would your lab do with the savings? #Basepair #Genomics #RNASeq #Bioinformatics #LabTech #ResearchInnovation #NGS #GenomicsWorkflow #ClinicalGenomics #DataAnalysis #LifeSciences

Financial institutions need to deliver faster, smarter, and more transparent service. Our blog shows how a multi-agent complaint system, built on MongoDB, Voyage AI by MongoDB, and Confluent, resolves common issues in seconds. 👇 https://lnkd.in/ejAgeEvM

Excited to launch my Black-Scholes Option Pricing dashboard! Built with PyTorch neural networks for real-time European option pricing and Greeks calculation. Frontend: Streamlit  (deployed on Streamlit Community Cloud) : https://lnkd.in/gkAFRWJj Backend: FastAPI (deployed on Render) : https://lnkd.in/gmASqg8u Code: https://lnkd.in/gRznxFTq Features: 1. Feedforward PyTorch architecture (ELU activation, batch normalisation, dropout) 2. Multi-layer regression for option pricing 3. Automatic differentiation for Greeks (Delta, Gamma, Rho, Vega, Theta) 4. Model checkpointing, serialisation, and reproducibility Note: If the server sleeps (Render free tier), you’ll see a “waking up” message—just wait a minute! #PyTorch #NeuralNetworks #DeepLearning #QuantitativeFinance #FinTech #OptionsPricing #Streamlit #FastAPI #MachineLearning

Financial institutions need to deliver faster, smarter, and more transparent service. Our blog shows how a multi-agent complaint system, built on MongoDB, Voyage AI by MongoDB, and Confluent, resolves common issues in seconds. 👇 https://lnkd.in/epz3ceA9

RAG promises to ground LLMs, but often introduces its own set of performance bottlenecks that can make real-time, multimodal applications feel sluggish and expensive, even on robust cloud infrastructure. Stephen Batifol’s recent InfoQ presentation dives deep into the strategic art of building and optimizing self-hosted, multimodal RAG systems. He meticulously breaks down the intricacies of vector search, from the foundational FLAT indexes to the efficiency gains of IVF and HNSW. A critical takeaway? The profound impact of choosing the right embedding model – a decision that significantly influences both retrieval accuracy and operational cost on platforms like AWS or GCP. Stephen then demystifies vLLM inference optimization, explaining how to achieve unparalleled throughput and low latency. This isn't just about scaling; it's about smart resource utilization and architecting for peak performance in an AI-first world, enabling efficient deployment of cutting-edge Generative AI solutions. What are your biggest challenges in deploying performant RAG systems today? Share your perspective below. Source: InfoQ.com URL: https://lnkd.in/gqJepFZb #RAG #vLLM #LLMOps #AI #MachineLearning #GenerativeAI #DevOps #VectorSearch #CloudComputing

Financial institutions need to deliver faster, smarter, and more transparent service. Our blog shows how a multi-agent complaint system, built on MongoDB, Voyage AI by MongoDB, and Confluent, resolves common issues in seconds. 👇 https://lnkd.in/dJsykwgN

Understanding HPC, MP, and vector processing as layers in a hierarchy clarifies their relationship. HPC provides the vision and the infrastructure—the Why and Where. MP delivers the scaling mechanism—the How at the system level. Vector processing supplies the mathematical horsepower—the how at the chip level. Together, they form the invisible foundation of modern AI and cloud services, enabling society to process knowledge, simulate worlds, and even converse with machines as if they were human. Without this triad, our current wave of AI breakthroughs would remain science fiction. https://lnkd.in/gftGxaRh