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Title: James Gilbert - Mercari US | LinkedIn

Open Graph Title: James Gilbert - Mercari US | LinkedIn

X Title: James Gilbert - Mercari US | LinkedIn

Description: Experience: Mercari US · Education: Georgia Institute of Technology · Location: New York · 500+ connections on LinkedIn. View James Gilbert’s profile on LinkedIn, a professional community of 1 billion members.

Open Graph Description: Experience: Mercari US · Education: Georgia Institute of Technology · Location: New York · 500+ connections on LinkedIn. View James Gilbert’s profile on LinkedIn, a professional community of 1 billion members.

X Description: Experience: Mercari US · Education: Georgia Institute of Technology · Location: New York · 500+ connections on LinkedIn. View James Gilbert’s profile on LinkedIn, a professional community of 1 billion members.

Opengraph URL: https://www.linkedin.com/in/james-gilbert-ds

X: @Linkedin

direct link

Domain: www.linkedin.com


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Meet Josh, our Global Quality Assurance Manager, based at Bradken’s foundry in Wodonga, Australia. From starting as a scholarship student 🎓 to… https://www.linkedin.com/posts/bradkenofficial_bradken-optimisedfuture-deliveringanoptimisedfuture-activity-7401783601786507264-GX0o
James Gilberthttps://www.linkedin.com/in/james-gilbert-ds?trk=public_profile_actor-name
Moment is hiring a Software Engineer, Trading (Backend, Senior) https://lnkd.in/e5mYK9MV via @greenhouse https://www.linkedin.com/posts/pywoo_software-engineer-trading-backend-senior-activity-7300184934462181376-Bq1u
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I attended the Missouri Library Association conference this week with several Johnson County Library coworkers! I loved hearing from so many library… https://www.linkedin.com/posts/ocusanelli_i-attended-the-missouri-library-association-activity-7245503963003830274-wj4t
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Aman Manchanda “James joined my team as a senior Backend Engineer and soon became a sought-after thought leader and mentor. He led some of the company’s most complex & important projects while simultaneously consulting on others. He frequently shared his knowledge of industry trends and best practices with the team and elevated design discussions. His contributions to our tools and processes helped scale the backend team while his background and experience as a Data Engineer bridged the practices to deliver innovative solutions. I would recommend James for any high performance, data-driven company looking to scale their engineering team with a versatile, multidisciplinary engineering leader.” https://www.linkedin.com/in/aman-manchanda?trk=public_profile_recommendations
Norman Chui “James is a great blend of talents. We hired him to be a business analyst intern, and he exceeded our expectations in that role. Given minimal direction, he has good common sense and instincts to figure out how to proceed in a variety of projects. Time and again, I find the approach he has taken to be logical, methodical, and thorough. James delivered on a number of projects for us. He built visualizations for several of our companies using Tableau. He configured Saleforce for another of our companies which involved a good bit of data cleansing, manipulating and importing. He built an attractive and compelling website for one of our new businesses. He was even able to conduct research and software selections for us that demonstrated yet another facet of his skills. Perhaps, one of the keys to his success is that he has already the beginnings of a strong hybrid foundation in business, technology, and consultings skills. I recommend James based on his track record of project delivery for us, his self-inititative, and that he is also a pleasure to work with. ” https://www.linkedin.com/in/normanchui?trk=public_profile_recommendations
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I'm honored to announce I've been selected as a HashiCorp Core Contributor 2024 for my contributions to the Terraform provider for Google Cloud… https://www.linkedin.com/posts/shotarok_hashicorp-core-contributor-2024-expired-activity-7184375169027637251-p-g4
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NVIDIA AI 2M followers https://www.linkedin.com/showcase/nvidia-ai/?trk=public_profile_relatedPosts_face-pile-cta
Get faster and smarter MOE inference straight out of the box. 👇Deep dive on scaling expert parallelism with TensorRT-LLM. LLMs with MOE promise higher model capacity without linearly increasing compute costs - but they introduce new challenges -- more conditional computation, dynamic routing, and non-uniform GPU utilization -- solved by TensorRT-LLM. ✨ New in TensorRT-LLM has native support for expert parallelism—designed for fast, efficient inference with MoE models like Mixtral (Mistral AI) and DeepSeek (DeepSeek AI). This gives you: ✅ Dynamic expert routing: Automatically route tokens to the top-k experts with minimal overhead. ✅ Efficient expert scheduling: Balance expert loads across GPUs using smart sharding and token bucketization. ✅ Memory-aware execution: Maximize hardware utilization while respecting memory budgets. ✅ Drop-in support: Use @HuggingFace models with minimal code changes via TensorRT-LLM's #Python API. 🧠 How it works: MoE models activate only a subset of "experts" for each token. This dynamic nature is powerful—but hard to optimize. It’s all done under the hood using custom #CUDA kernels and NCCL-based communication primitives—giving you low latency, high throughput, and better GPU scaling. ✨ TensorRT-LLM handles: ✅Token-expert mapping using the gating network. ✅Token sorting to batch same-expert tokens together. ✅Expert parallel execution across GPUs. ✅Merging outputs for final predictions. 🛠️ Developer workflow - here is the code to get started. # Clone the repo git clone https://lnkd.in/g-GiDX23 # Use included examples to load and run a Mixtral model cd TensorRT-LLM/examples/mixtral From there, the Python API lets you load the model, convert it with TensorRT, and run expert parallel inference—all with a few lines of code. Results? 📈 Performance at scale. Tests show up to 2.3x faster inference throughput compared to standard tensor parallelism when using 8 GPUs and top-2 experts per token. Even better—TensorRT-LLM keeps efficiency high across increasing batch sizes. Want to see it in action or contribute? 👉 Read the full tech blog: https://lnkd.in/g_7YV3vV 👉 Explore the code on GitHub: https://lnkd.in/gNjQ5W2U 👉 Follow updates in the TensorRT-LLM repo: https://lnkd.in/gqSHYQ4u Share your experiences with us. 78 3 Comments https://www.linkedin.com/posts/nvidia-ai_python-cuda-activity-7340831358732181505-yWHo?trk=public_profile_relatedPosts
PyTorch 308K followers https://www.linkedin.com/company/pytorch?trk=public_profile_relatedPosts_face-pile-cta
We’re excited to welcome Ray to the PyTorch Foundation 👋 Ray is an open source distributed computing framework for AI workloads, including data processing, model training and inference at scale. By contributing Ray to the PyTorch Foundation, Anyscale reinforces its commitment to open governance and long-term sustainability for Ray and open source AI.    “The PyTorch Foundation is committed to fostering an open, interoperable, and production-ready AI ecosystem. By bringing Ray under the PyTorch Foundation umbrella, alongside projects like vLLM and DeepSpeed, we are uniting the critical components needed to build next-generation AI systems. Ray’s inclusion strengthens our collective mission to support developers with the tools to efficiently train, serve, and deploy AI models at scale.” - Matt White GM of AI at The Linux Foundation Foundation and Executive Director of the PyTorch Foundation “With Ray, our goal is to make distributed computing as straightforward as writing Python code. Joining the PyTorch Foundation helps us stay true to that mission, ensuring Ray continues to be an open, community-driven backbone for developers and their organizations.” - Robert Nishihara, co-founder of Anyscale ➡️ Read the announcement: https://lnkd.in/e3e4mjds #Ray #PyTorchCon #PyTorch #AI #OpenSource #AICompute #DistributedComputing #AIWorkloads #DataProcessing #ModelTraining #Inference 667 5 Comments https://www.linkedin.com/posts/pytorch_ray-pytorchcon-pytorch-activity-7386795977589923840-n0St?trk=public_profile_relatedPosts
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Datadog announces the general availability of Bits AI SRE, the company’s first AI agent. Powered by the full breadth and depth of the Datadog platform’s data, Bits AI SRE autonomously investigates alerts and pinpoints root causes in minutes. What used to take hours to troubleshoot manually now happens autonomously, helping teams shift their time from managing incidents to building resilient systems. “This launch represents a pivotal expansion of Datadog’s AI strategy as our first generally available AI agent, and signals a new phase of intelligent, automated reliability.” — Yanbing Li, Chief Product Officer at Datadog Learn more in our press release: https://lnkd.in/epqgncTr 407 2 Comments https://www.linkedin.com/posts/datadog_datadog-announces-the-general-availability-activity-7401674321967980545-sePd?trk=public_profile_relatedPosts
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Excited to announce Ray on Anyscale is now available on CoreWeave to further deliver on our multi-cloud support. This gives users access to CoreWeave’s purpose-built AI infrastructure, optimized for distributed AI at scale with low latency, fast startup times, and high performance GPUs. It's a powerful combination for teams building and scaling production AI workloads. Read more from CoreWeave: https://lnkd.in/dN9AjA3s 83 1 Comment https://www.linkedin.com/posts/joinanyscale_announcing-distributed-ai-on-coreweave-with-activity-7360702988975247360-rOZ7?trk=public_profile_relatedPosts
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This is a great writeup that summarizes the different storage approaches used by Datadog over the years and then goes into detail about the newest (v6) engine. LSM and timeseries go well together. https://lnkd.in/g8yTkxuA 97 1 Comment https://www.linkedin.com/posts/mdcallag_evolving-our-real-time-timeseries-storage-activity-7366557282379169795-72CP?trk=public_profile_relatedPosts
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Our friends over at Discord incredible engineering blog on how they're processing petabytes of data with dbt Labs and Dagster while supporting 100+ developers working across 2,500+ models! Key Highlights: 📦 Environment separation strategy to prevent developers from overwriting each other's test tables 📈 Performance optimizations that achieved a 5x speedup in dbt execution times 🧠 Smart versioning system for automated, efficient backfills ⚙️ Comprehensive macros and CI/CD guardrails for data quality Great work Chris D. ! Link in the comments. 51 1 Comment https://www.linkedin.com/posts/dagsterlabs_our-friends-over-at-discord-incredible-engineering-activity-7318282742230630400-28fY?trk=public_profile_relatedPosts
Boran Han Amazon Web Services (AWS) • 1K followers https://www.linkedin.com/in/boran-han-ba753966?trk=public_profile_relatedPosts_face-pile-cta
🚀 We’re excited to share our latest work “MLZero: A Multi-Agent System for End-to-End Machine Learning Automation” is now available on arXiv! 👉 Read the paper: https://lnkd.in/g8WixK_U In this work, we introduce MLZero, a multi-agent framework that automates the machine learning workflow across diverse data modalities — with minimal human intervention. 🔆 Key highlights: 1️⃣ A cognitive perception module first transforms raw multimodal inputs into perceptual context that guides the workflow intelligently. 2️⃣ We enhance the iterative code generation process with semantic and episodic memory. 3️⃣ MLZero, outperforms all competitors in both success rate and solution quality, securing six gold medals on MLE-Bench Lite. 4️⃣ On our 25 challenging tasks spanning diverse modalities, MLZero achieves a 0.92 success rate and an average rank of 2.28, ahead of alternatives. 🔧 Even better: MLZero is integrated into the newly released AutoGluon Assistant 1.0 — providing a user-friendly WebUI for AutoML. Code and documentation: https://lnkd.in/gDUMVQx6 To get started: pip install autogluon.assistant You can now simply upload your dataset, describe your task in plain English, and let MLZero handle the rest! Thank you to all collaborators and contributors who made this possible! Haoyang Fang Steven Shen Nick Erickson Xiyuan Zhang Su Zhou Anirudh Dagar Jiani ZHANG Caner Türkmen Cuixiong (Tony) Hu Huzefa Rangwala Ying-Nian Wu, Yuyang (Bernie) Wang George Karypis #MLZero #AutoML #LLM #AI #MachineLearning #OpenSource #AutoGluon 125 8 Comments https://www.linkedin.com/posts/boran-han-ba753966_mlzero-automl-llm-activity-7349577170005999616-FCtl?trk=public_profile_relatedPosts
Amit Yadav Ishiki Labs • 10K followers https://www.linkedin.com/in/amit-kumar-singh-yadav?trk=public_profile_relatedPosts_face-pile-cta
Excited to share about two manuscripts to be presented at ICASSP 2025! 🎉 1️⃣ Speech-N-LlaMA: Solving the Exposure Bias Problem in Speech LLMs 🤖 🤝 Research done with LlaMA Speech team, AI at Meta The challenge: LLMs are sequence-to-sequence networks that use teacher forcing (golden output) during training, but inference happens auto-regressively → creating an "exposure bias" gap! Our solution: N-pass decoding during training to bridge this gap Results: Up to 18% relative WER improvement with zero additional inference compute! 2️⃣ DiffSSD: Catching Up to Diffusion-Based Speech Generators 🤝 Done during and w/ PhD collaborators at Purdue University and ISPL - Politecnico di Milano The problem: Showed current synthetic speech detectors are falling behind! They don't generalize to recent diffusion-based generators Our solution: DiffSSD - a new 200-hours dataset featuring speech from both open-source and commercial diffusion-based generators Impact: Detectors trained with this dataset can counter synthetic speech from the latest speech generator tools ICASSP proceedings can be found in comment. Thanks to collaborators Gil, Desh, Ozlem, and all peers from LlaMA Speech team, Kratika, Prof. Delp, and Prof. Bestagini. #AI #SpeechLLMs #GenerativeAI #Deepfake #LLMs 177 5 Comments https://www.linkedin.com/posts/amit-kumar-singh-yadav_ai-speechllms-generativeai-activity-7305806164187885570-l599?trk=public_profile_relatedPosts
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