UbiOps
UbiOpsとは何ですか？
UbiOpsは、AI＆MLワークロードを確実かつ安全なマイクロサービスとして素早く実行するためのAIインフラストラクチャプラットフォームです。既存のワークフローを大幅に変更することなく、データサイエンスワークベンチにシームレスに統合でき、高価なクラウドインフラストラクチャを管理する負担を取り除きます。
UbiOpsの使い方は？
UbiOpsを使用して、簡単にスケーラブルなAI製品を展開できます。データサイエンスワークベンチに簡単に統合し、時間のかかるクラウドインフラストラクチャのセットアップと管理を回避します。
UbiOpsのコア機能
プロダクショングレードのAI/MLワークロードの素早い展開
スケーラブルなAIモデルの提供とオーケストレーション
高度なAI製品のための組み込み機能
UbiOpsの使用例
スタートアップや大規模組織向けのAI製品の開発
インフラストラクチャの心配なく信頼性の高いAIやMLサービスを可能にする
UbiOpsのFAQ
UbiOpsで展開できるものは何ですか？
UbiOpsは機密データに対してどれだけ安全ですか？
UbiOps サポートメール、カスタマーサービス連絡先、返金連絡先など
さらに連絡するには、お問い合わせページ (https://ubiops.com/contact-us/) にアクセスしてください。
UbiOps 会社
UbiOps 会社名: Dutch Analytics B.V. 。
UbiOps 会社の住所: LAB42, room L2.16, Science Park 900, 1098 XH Amsterdam, the Netherlands The Hague office Wilhelmina van Pruisenweg 35 2595 AN, The Hague The Netherlands +31 70 792 00 91 New York office 228 E. 45th St., Suite 9E New York, NY 10017。
UbiOps について詳しくは、会社概要ページ (https://ubiops.com/about-us/) をご覧ください。 。
UbiOps ログイン
UbiOps ログイン リンク: https://app.ubiops.com/sign-in
UbiOps サインアップ
UbiOps サインアップ リンク: https://app.ubiops.com/sign-up/
UbiOps のYoutube
UbiOps Youtubeリンク: https://youtube.com/channel/UCQBpeuKmGcRWptc2Ldumyrw
UbiOps のLinkedin
UbiOps Linkedinリンク: https://linkedin.com/company/ubiops
UbiOps のTwitter
UbiOps Twitterリンク: https://twitter.com/UbiOps_
UbiOps のGithub
UbiOps Githubリンク: https://github.com/UbiOps
UbiOps レビュー (0)
UbiOps の分析
UbiOps ウェブサイトのトラフィック分析
地理的なトラフィック
上位5地域
UbiOps 埋め込みを起動する
ウェブサイトバッジを使用して、Toolify Launchに対するコミュニティからのサポートを促進しましょう。ホームページやフッターに簡単に埋め込むことができます。
ソーシャルリスニング
Deploy Llama 3 in 5 minutes (tutorial)
Hey there, data scientists! 🌟 In today’s tutorial, we’re deploying Meta’s latest large language model, Llama 3, on UbiOps in under 15 minutes. Llama 3 is the newest addition to Meta's Llama series, offering impressive capabilities with its 8 billion parameter version. Whether you're looking to harness its power for advanced tasks or just exploring its potential, this tutorial will help get you started. In this step-by-step guide, we'll walk you through every detail, ensuring you can deploy the Llama 3 8B instruct model effortlessly. Plus, discover tips on building a user-friendly front-end for your chatbot using Streamlit! You can also follow the detailed, written version of this tutorial here: https://ubiops.com/deploy-llama3-with-ubiops/ 🔍 Learn how to: Set up a UbiOps account with GPU access Create a custom environment for your model Set up and configure your deployment Make inference requests to test your deployed model ⚙️ To successfully complete this guide, you will need: UbiOps account with GPU access Supporting files and code snippets included in the full tutorial: https://ubiops.com/deploy-llama3-with-ubiops/ 🚀 UbiOps: - Free trial account sign-up: https://app.ubiops.com/sign-up/ - Slack community: https://join.slack.com/t/ubiops-community/shared_invite/zt-np02blts-5xyFK0azBOuhJzdRSYwM_w - Contact form: http://ubiops.com/contact-us/ - Blog page for more guides: https://ubiops.com/blog/ - Documentation: https://ubiops.com/docs/ 🎥 Don't miss out on this opportunity to level up your AI and machine learning game. Hit that like button, share with your fellow tech enthusiasts, and subscribe to stay updated on our latest tutorials and insights. Happy coding! 🚀 Chapters: 0:00 Introduction 0:20 What's Llama 3? 0:55 Create UbiOps account 1:12 Create environment 1:26 Create deployment 1:53 Create version 2:12 Hugging Face token 2:43 Make inference request 3:07 Streamlit front-end 3:36 Conclusion #AI #MachineLearning #Llama3 #UbiOps #Tutorial #Tech #DataScience #Chatbot #Deployment
Fine-tune Mistral 7b on your own documents in under 5 minutes
Welcome back, data enthusiasts! 🌟 In today's tutorial, we're diving deep into the realm of fine-tuning to craft a domain-expert AI assistant. In this tutorial, we'll guide you through each step, from setting up your accounts and environments to preprocessing your data and executing the fine-tuning process. Whether you're a seasoned data scientist or just starting out, this hands-on guide will equip you with the skills to create a customized chatbot tailored to your unique use case. Join us as we explore the intricacies of Parameter-Efficient Fine-Tuning (PEFT) with Low Rank Adaptation (LoRA) to retrain an open-source Large Language Model (LLM) on UbiOps documentation. This is a faster, cheaper, and less resource-intensive fine-tuning method. You can also follow the detailed, written version of this tutorial here: https://ubiops.com/fine-tune-a-model-on-your-own-documentation/ 🔍 Learn how to: - Create a UbiOps and HuggingFace account to gain access to the Mistral-7b-instruct-v0.2 model. - Prepare documents to be used as training data. - Initiate a training run to fine-tune the model. ⚙️ To successfully complete this guide, you will need: - UbiOps account with training functionality enabled (see below) - Supporting files and code snippets included in the full tutorial: https://ubiops.com/implementing-rag-for-your-llm-mistral/ 🚀 UbiOps: - Free trial account sign-up: https://app.ubiops.com/sign-up/ - Slack community: https://join.slack.com/t/ubiops-community/shared_invite/zt-np02blts-5xyFK0azBOuhJzdRSYwM_w - Contact form: http://ubiops.com/contact-us/ - Blog page for more guides: https://ubiops.com/blog/ - Documentation: https://ubiops.com/docs/ 🎥 Don't miss out on this opportunity to level up your AI and machine learning game. Hit that like button, share with your fellow tech enthusiasts, and subscribe to stay updated on our latest tutorials and insights. Happy coding! 🚀 Chapters: 0:00 Introduction 1:39 Create accounts 1:52 Prepare training data 2:40 Fine-tune the model 3:17 Test the model 3:54 Conclusion #AI #MachineLearning #finetuning #mistral #LLM #Tutorial #Tech #DataScience #UbiOps
Deploy LLaMA 2 with a Streamlit front-end in under 15 minutes (including CPU vs GPU benchmark)
In this guide, we explain how to deploy LLaMA 2, an open-source Large Language Model (LLM), using UbiOps for easy model hosting and Streamlit for creating a chatbot UI. The guide provides step-by-step instructions for packaging a deployment, loading it into UbiOps, configuring compute on GPUs and CPUs, generating API tokens, and integrating with Streamlit for the front-end. We conclude with a benchmark test showing that GPUs can provide over 30x faster processing speeds than CPUs. This guide aims to make cutting-edge AI accessible by allowing anyone to deploy their own LLaMA 2 chatbot in minutes. To successfully complete this guide, you will need: - Python 3.9 or higher installed - Streamlit library installed - UbiOps Client Library installed (see below) - UbiOps account (see below) Here are some useful links to support you: ⚒️ Materials: - Written “Deploy LlaMA 2” guide: https://ubiops.com/deploy-llama-2-with-a-customizable-front-end-in-under-15-minutes-using-only-ubiops-python-and-streamlit/#unique-identifier - HuggingFace LLaMA 2-7b model authorization: https://huggingface.co/meta-llama/Llama-2-7b-hf - UbiOps documentation on deployment package structure: https://ubiops.com/docs/deployments/deployment-package/deployment-structure/ - Streamlit + LLaMA tutorial: https://blog.streamlit.io/how-to-build-a-llama-2-chatbot/ - UbiOps + Streamlit integration tutorial: https://ubiops.com/docs/ubiops_tutorials/streamlit-tutorial/streamlit-tutorial/ - More info on LlaMA 2: https://ai.meta.com/llama/ 🚀 UbiOps: - Free account sign-up: https://app.ubiops.com/sign-up/ - Slack community: https://join.slack.com/t/ubiops-community/shared_invite/zt-np02blts-5xyFK0azBOuhJzdRSYwM_w - Contact form: http://ubiops.com/contact-us/ - Blog page for more guides: https://ubiops.com/blog/ Chapters: 0:00 - Overview 0:57 - Getting started 1:55 - Build deployment package 4:19 - Load & configure deployment 6:02 - Build front-end 7:40 - Prompt your model 8:51 - CPU vs GPU benchmark 10:44 - Final thoughts #chatgpt #promptengineering #chatbot #llama #artificialintelligence #python #huggingface -------------------- More useful UbiOps content below: Website: https://ubiops.com/ Blog: https://ubiops.com/blog/ Documentation: https://ubiops.com/docs/ Instant models: https://ubiops.com/community-models-ubiops/
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