Loading...
Model serving, experiment tracking, CI/CD for ML, and production systems
Follow this topic to see new discussions in your feed.
Join the AI professionals discussing this topic
Create a free Aivimat Connect account to follow this topic, reply to discussions, save useful posts, and start your own conversations.
Start a structured post in MLOps & Deployment for the Aivimat Connect community.
Ranked by reactions, comments, and reposts in MLOps & Deployment.
Our latest paper on emergent world models in large language models just got accepted at ICML 2026! 🎉 Key finding: When you train LLMs on enough diverse data, they develop internal representations that look remarkably like simplified physics engines. This has huge implications for how we think about grounding and embodied AI.
Just finished interviewing 50+ AI engineers about their tech stack. Consensus stack for 2026: • Models: GPT-4o + Claude 3.5 Sonnet + Llama 4 (open) • Frameworks: LangChain or LlamaIndex • Vector DB: Pgvector (startups) / Pinecone (enterprise) • Orchestration: Temporal or Inngest • Monitoring: W&B + LangSmith • Deployment: Replicate, Modal, or self-hosted The stack is maturing fast.
We just raised our Series B! NeuralScale is building the infrastructure layer that will power the next generation of AI applications. Think of us as 'Vercel for AI inference' — but with 10x better latency and 3x lower cost than existing solutions. Hiring across the board: infra engineers, ML engineers, and product managers. DM me or check the jobs page.
Dell says enterprises don’t have an AI ambition problem — they have an AI execution problem Dell Technologies has published a major enterprise AI update around Dell AI Factory with NVIDIA, focused on moving companies from AI pilots to real production outcomes. This is exactly the conversation AI professionals should be having now. Most companies already know they need AI. The bigger problem is...
www.dell.com
https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releasesusa202605dell-technologies-closes-the-gap-between-ai-ambition-and-ai-outcomes.htm
Model serving, experiment tracking, CI/CD for ML, and production systems
Explore adjacent topic hubs on Aivimat Connect — such as machine learning, generative AI, AI safety, or MLOps — to broaden context around MLOps & Deployment. Use subtopics and related links above when available.
Dell says enterprises don’t have an AI ambition problem — they have an AI execution problem Dell Technologies has published a major enterprise AI update around Dell AI Factory with NVIDIA, focused on moving companies from AI pilots to real production outcomes. This is exactly the conversation AI professionals should be having now. Most companies already know they need AI. The bigger problem is...
www.dell.com
https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releasesusa202605dell-technologies-closes-the-gap-between-ai-ambition-and-ai-outcomes.htm
What technologies power fast-moving viral campaigns online? When a viral movement grows quickly, most people only see the Instagram posts, memes, and public attention. But behind any fast-moving digital campaign, there is usually a stack of tools and execution choices. A simple viral campaign may need: * social media publishing tools * short-form video editing tools * image and meme generation...
The new FLUX model is genuinely impressive for real-time video understanding. I've been benchmarking it against our production pipeline at Tesla. Key observations: • 3x faster inference than previous SOTA • Better temporal consistency across frames • Still struggles with fine-grained action recognition • Impressive zero-shot performance on edge cases Anyone else running comparisons?
Dell says enterprises don’t have an AI ambition problem — they have an AI execution problem Dell Technologies has published a major enterprise AI update around Dell AI Factory with NVIDIA, focused on moving companies from AI pilots to real production outcomes. This is exactly the conversation AI professionals should be having now. Most companies already know they need AI. The bigger problem is...
www.dell.com
https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releasesusa202605dell-technologies-closes-the-gap-between-ai-ambition-and-ai-outcomes.htm
What technologies power fast-moving viral campaigns online? When a viral movement grows quickly, most people only see the Instagram posts, memes, and public attention. But behind any fast-moving digital campaign, there is usually a stack of tools and execution choices. A simple viral campaign may need: * social media publishing tools * short-form video editing tools * image and meme generation...
The new FLUX model is genuinely impressive for real-time video understanding. I've been benchmarking it against our production pipeline at Tesla. Key observations: • 3x faster inference than previous SOTA • Better temporal consistency across frames • Still struggles with fine-grained action recognition • Impressive zero-shot performance on edge cases Anyone else running comparisons?
We just raised our Series B! NeuralScale is building the infrastructure layer that will power the next generation of AI applications. Think of us as 'Vercel for AI inference' — but with 10x better latency and 3x lower cost than existing solutions. Hiring across the board: infra engineers, ML engineers, and product managers. DM me or check the jobs page.
Our latest paper on emergent world models in large language models just got accepted at ICML 2026! 🎉 Key finding: When you train LLMs on enough diverse data, they develop internal representations that look remarkably like simplified physics engines. This has huge implications for how we think about grounding and embodied AI.
What technologies power fast-moving viral campaigns online? When a viral movement grows quickly, most people only see the Instagram posts, memes, and public attention. But behind any fast-moving digital campaign, there is usually a stack of tools and execution choices. A simple viral campaign may need: * social media publishing tools * short-form video editing tools * image and meme generation...
The new FLUX model is genuinely impressive for real-time video understanding. I've been benchmarking it against our production pipeline at Tesla. Key observations: • 3x faster inference than previous SOTA • Better temporal consistency across frames • Still struggles with fine-grained action recognition • Impressive zero-shot performance on edge cases Anyone else running comparisons?
We just raised our Series B! NeuralScale is building the infrastructure layer that will power the next generation of AI applications. Think of us as 'Vercel for AI inference' — but with 10x better latency and 3x lower cost than existing solutions. Hiring across the board: infra engineers, ML engineers, and product managers. DM me or check the jobs page.
Our latest paper on emergent world models in large language models just got accepted at ICML 2026! 🎉 Key finding: When you train LLMs on enough diverse data, they develop internal representations that look remarkably like simplified physics engines. This has huge implications for how we think about grounding and embodied AI.
Just finished interviewing 50+ AI engineers about their tech stack. Consensus stack for 2026: • Models: GPT-4o + Claude 3.5 Sonnet + Llama 4 (open) • Frameworks: LangChain or LlamaIndex • Vector DB: Pgvector (startups) / Pinecone (enterprise) • Orchestration: Temporal or Inngest • Monitoring: W&B + LangSmith • Deployment: Replicate, Modal, or self-hosted The stack is maturing fast.
Sign in to follow topics and post in discussions.