#BentoFriday 🍱 — Lifecycle Hooks in #BentoML When deploying real-world #AI services, the work doesn’t end at inference. You also need to manage everything that happens before, during, and after a request hits your model. In practice, that often means: 🔧 Setting up global

Enterprises can’t scale #AI inference without compromises. The 𝗚𝗣𝗨 𝗖𝗔𝗣 𝗧𝗵𝗲𝗼𝗿𝗲𝗺 says you can’t have all three at once: 🔒 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 over your models & data and compliance ⚡ 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 to scale on demand when traffic spikes 💰 𝗣𝗿𝗶𝗰𝗲 that keeps

🚀 Self-host models with #Triton + #BentoML! NVIDIA Triton Inference Server is a powerful open-source tool for serving models from major ML frameworks like ONNX, PyTorch, and TensorFlow. This project wraps Triton with BentoML, making it easy to: 🎯 Package custom models as

#BentoFriday 🍱 — 20x Faster Iteration with BentoML Codespaces Modern #AI apps like #RAG or voice agents often require multiple powerful GPUs and complex dependencies. This often leads to: ❌ Painstaking delays with each code change ❌ Challenging environment setups ❌

🚀 Build CI/CD pipelines for #AI services with #BentoML + #GitHubActions Automate everything with pipelines that: ✅ Deploy services to #BentoCloud ✅ Trigger on code or deployment config changes ✅ Wait until the service is ready ✅ Run test inference 📘 Step-by-step guide:

Want to self-host model inference in production? Start with the right model. We’ve put together a series exploring popular open-source models. Ready to deploy with #BentoML 🍱 🗣️ Text-to-Speech bentoml.com/blog/exploring… 🖼️ Image Generation bentoml.com/blog/a-guide-t… 🧠 Embedding

#BentoFriday 🍱 — Inference Context with 𝘣𝘦𝘯𝘵𝘰𝘮𝘭.𝘊𝘰𝘯𝘵𝘦𝘹𝘵 Building #AI/ML APIs isn’t just about calling a model. You need a clean, reliable way to customize your inference service. 𝘣𝘦𝘯𝘵𝘰𝘮𝘭.𝘊𝘰𝘯𝘵𝘦𝘹𝘵 is one of those abstractions in #BentoML that gives

🚀 DeepSeek-R1-0528 just landed! 🔍 Still no official word — no model card, no benchmarks. #DeepSeek being DeepSeek, as always 😅 ✅ Good news: #BentoML already supports it. 👉 Deploy it now with our updated example: github.com/bentoml/BentoV… 👀 Follow for more updates!

👀 Update on DeepSeek-R1-0528 bentoml.com/blog/the-compl… 🧠 Built on V3 Base 📈 Major reasoning improvements 🛡️ Reduced hallucination ⚙️ Function calling + JSON output 📦 Distilled Qwen3-8B beats much larger models 📄 Still MIT See our updated blog ⬇️ #AI #LLM #BentoML #OpenSource

Choosing the right #AI deployment platform? Check out our detailed comparison of #BentoML vs #VertexAI to help you make informed decisions. bentoml.com/blog/compariso… 🔍 Here’s what we cover: ✅ Cloud infrastructure flexibility ✅ Scaling and performance ✅ Developer experience and

#BentoFriday 🍱 — Runtime Specs in Pure Python Deploying #AI services isn’t just about your model code; it’s also about getting the right runtime and making sure it is reproducible across environments. That might include: 🐍 Python version 🖥️ OS & system packages 📦 Python

🚀 #Magistral, Mistral AI’s first reasoning model, is here and now deployable with #BentoML! This release features two variants: - Magistral Small: 24B parameter open-source version - Magistral Medium: Enterprise-grade, high-performance version Highlights of Magistral Small: 🔧

We’re entering a new era for #LLMInference: moving beyond single-node optimizations to distributed serving strategies that unlock better performance, smarter resource utilization, and real cost savings. In this blog post, we break down the latest techniques for distributed LLM

#BentoFriday 🍱 — Add a Web UI with Gradio Real-world #AI apps don’t just need a model. They need interfaces users can interact with. But building a custom frontend is time-consuming and managing it separately from your backend adds unnecessary complexity. 😵‍💫 With #BentoML,

🍱Spreading the word on #BentoML at #KubeCon + #CloudNativeCon 🙌 Big shoutout to FogDong

#BentoFriday 🍱 — WebSocket Endpoints Real-time #AI apps like voice assistants and live chatbots need more than just REST APIs. They need persistent, low-latency connections. ⚡ But spinning up a separate #WebSocket server just for that? ❌ Duplicated infra ❌ Complex routing

#BentoFriday 🍱 — Lightning-Fast Model Loading When deploying #LLM services, slow model loading can cripple your cold starts. 🚨 This leads to delayed autoscaling, missed requests during traffic spikes, and a poor user experience. #BentoML supercharges model loading with speed

Your LLM demo wowed the execs. It’s time to ship it. Now comes the hard part: scaling inference efficiently, optimizing inference performance, and managing heterogeneous LLM workflows and compute environments. But this is not an easy feat. It requires a set of best practices

🚀 Try Canary Deployments on our #BentoInferencePlatform! Don’t risk regressions or broken user experiences when rolling out new model versions. ✅ Deploy multiple Bento versions at once 🎯 Smart traffic routing strategies 📊 Real-time monitoring for different versions ⏱️

Modern #AI agents don’t just generate text. They write and run code. But what happens when: ⚠️ A prompt injection triggers harmful logic? ⚠️ An agent runs unknown scripts from a Git repo? ⚠️ It connects to untrusted APIs? Letting agents run arbitrary code without guardrails is