Machine learning at scale VS BuildShip
Machine learning at scale VS BuildShip 对比，Machine learning at scale 和 BuildShip 有什麼區別？
總結
Machine learning at scale 總結
Machine learning at scale: Learn about ML systems from top tech companies. Delivered once a week in your inbox.
Machine learning at scale 著陸頁
BuildShip 總結
Create AI Assistants on any model of your choice (OpenAI, Azure, Claude), connected to your tools and database (Firebase, Supabase, Notion, Google Sheets + custom tools generated with AI). Let AI take action for your users on your product and websites.
BuildShip 著陸頁
比較詳情
Machine learning at scale 詳細信息
| 類別 | 電子報, 大型語言模型(LLMs), AI 代碼助手, AI 開發者文件, AI開發工具, 程式碼解釋 |
| Machine learning at scale 網站 | https://machinelearningatscale.com |
| 添加時間 | 2023年5月22日 |
| Machine learning at scale 定價 | -- |
BuildShip 詳細信息
| 類別 | AI聊天機器人, 大型語言模型(LLMs), AI分析助手, 寫作助手, AI語音助手, 無碼&低碼開發 |
| BuildShip 網站 | https://aibot.how |
| 添加時間 | 2024年5月4日 |
| BuildShip 定價 | -- |
使用對比
如何使用Machine learning at scale？
To access the content on Machine learning at scale, you can subscribe to their email newsletter. Once you subscribe, you will receive regular updates and gain access to members-only content. Simply click on the link provided in the confirmation email to complete your subscription. In addition, you can browse through their articles on the website, which cover a wide range of machine learning topics. The website is designed to provide insights and knowledge for individuals who are interested in understanding machine learning systems at scale.
如何使用BuildShip？
Start by cloning a template from the library, add your API key, integrate tools and database, and connect to ship as API or Chatbot with HTML embed widget
比較 Machine learning at scale 和 BuildShip 的優點
Machine learning at scale 的核心功能
- Email newsletter with members-only content
- In-depth articles on machine learning systems at scale
- Insights from top tech companies
- Topics include distributed training, feature stores, on-device models, robustness against adversarial examples, and more
BuildShip 的核心功能
- Connect to tools and databases
- Generate action nodes with AI
- No-code function calling
- Plugin chat widget to any website
- Create various types of bots
比較用例
Machine learning at scale 的用例
- Gaining insights into machine learning systems at a large scale
- Learning about distributed training frameworks
- Understanding the challenges and solutions of deploying on-device machine learning models
- Exploring techniques for robustness against adversarial examples
- Discovering different roles in the machine learning industry
- Staying updated with the latest trends and developments in machine learning
BuildShip 的用例
- City Advisor - Ask for plans in a specific city
- Chat with Assistant - Connect your OpenAI Assistant to BuildShip
- Assistant with Document Retrieval - Access files you upload in the Assistant playground
- Chat with GSheets - Access Google Sheets to respond
- Data Analyst - Recruit a data analyst for research
- Chat with your Database - Give recommendations to customers based on dishes
- Quiz Master - Chat with a history tutor and generate quizzes
- Website Q&A - Scrape a website and answer questions
- Email Assistant - Access contacts and send emails
Machine learning at scale 的流量
Machine learning at scale 是月访问量為 11.8K 且平均訪問時長為 00:00:05 的工具。 Machine learning at scale 的每次訪問頁數為 0.45，跳出率為 59.19%。
最新網站流量
| 月訪問量 | 11.8K |
| 平均訪問時長 | 00:00:05 |
| 每次訪問頁數 | 0.45 |
| 跳出率 | 59.19% |
BuildShip 的流量
BuildShip 是月访问量為 23.4K 且平均訪問時長為 00:00:27 的工具。 BuildShip 的每次訪問頁數為 0.60，跳出率為 84.22%。
最新網站流量
| 月訪問量 | 23.4K |
| 平均訪問時長 | 00:00:27 |
| 每次訪問頁數 | 0.60 |
| 跳出率 | 84.22% |
地理流量
The top 5 countries/regions for Machine learning at scale are:Turkey 6.31%, Colombia 6.22%, Russia 5.82%, Vietnam 5.66%, United States 5.52%
Top 5 Countries/regions
 | 6.31% |
 | 6.22% |
 | 5.82% |
 | 5.66% |
 | 5.52% |
地理流量
The top 5 countries/regions for BuildShip are:United States 9.16%, Vietnam 7.72%, Indonesia 7.09%, Turkey 6.93%, Russia 6.24%
Top 5 Countries/regions
| 9.16% |
| 7.72% |
 | 7.09% |
| 6.93% |
| 6.24% |
網站流量來源
Machine learning at scale 的 6 個主要流量來源是：直接 47.82%, 社群 40.68%, 引薦 11.50%, 郵件 0.00%, 自然搜尋 0.00%, 多媒體廣告 0.00%
直接 | 47.82% |
社群 | 40.68% |
引薦 | 11.50% |
郵件 | 0.00% |
自然搜尋 | 0.00% |
多媒體廣告 | 0.00% |
網站流量來源
BuildShip 的 6 個主要流量來源是：直接 77.83%, 社群 14.31%, 引薦 5.86%, 自然搜尋 2.00%, 郵件 0.00%, 多媒體廣告 0.00%
直接 | 77.83% |
社群 | 14.31% |
引薦 | 5.86% |
自然搜尋 | 2.00% |
郵件 | 0.00% |
多媒體廣告 | 0.00% |
Machine learning at scale 或 BuildShip哪個更好？
BuildShip 可能比 Machine learning at scale 更受歡迎。如您所見，Machine learning at scale 每月有 11.8K 次訪問，而 BuildShip 每月有 23.4K 次訪問。 所以更多的人選擇BuildShip。 因此，人們很可能會在社交平台上更多地推薦 BuildShip。
Machine learning at scale 的平均訪問持續時間為 00:00:05，而 BuildShip 的平均訪問持續時間為 00:00:27。 此外，Machine learning at scale 的每次訪問頁面為 0.45，跳出率為 59.19%。 BuildShip 的每次訪問頁面為 0.60，跳出率為 84.22%。
Machine learning at scale 的主要用戶是Turkey, Colombia, Russia, Vietnam, United States，分佈如下：6.31%, 6.22%, 5.82%, 5.66%, 5.52%。
BuildShip 的主要用戶是 United States, Vietnam, Indonesia, Turkey, Russia，分佈如下：9.16%, 7.72%, 7.09%, 6.93%, 6.24%。
查看其他对比
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