🔥 Trending Repository: mcp-context-forge
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 22
🍴 Forks: 184 forks
💻 Programming Languages: Python - JavaScript - Makefile - HTML - Go - Shell
🏷️ Related Topics:
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🧠 By: https://t.iss.one/DataScienceM
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 22
🍴 Forks: 184 forks
💻 Programming Languages: Python - JavaScript - Makefile - HTML - Go - Shell
🏷️ Related Topics:
#python #docker #kubernetes #devops #jwt #tools #ai #api_gateway #mcp #gateway #asyncio #federation #agents #observability #authentication_middleware #fastapi #prompt_engineering #generative_ai #llm_agents #model_context_protocol
==================================
🧠 By: https://t.iss.one/DataScienceM
❤2
🔥 Trending Repository: mcp-context-forge
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.7K stars
👀 Watchers: 21
🍴 Forks: 215 forks
💻 Programming Languages: Python - HTML - JavaScript - Makefile - Go - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.7K stars
👀 Watchers: 21
🍴 Forks: 215 forks
💻 Programming Languages: Python - HTML - JavaScript - Makefile - Go - Shell
🏷️ Related Topics:
#python #docker #kubernetes #devops #jwt #tools #ai #api_gateway #mcp #gateway #asyncio #federation #agents #observability #authentication_middleware #fastapi #prompt_engineering #generative_ai #llm_agents #model_context_protocol
==================================
🧠 By: https://t.iss.one/DataScienceM
🤖🧠 MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models
🗓️ 30 Oct 2025
📚 AI News & Trends
Machine Learning models are powerful but building them is only half the story. The true challenge lies in deploying, scaling and maintaining these models in production environments – a process that requires collaboration between data scientists, developers and operations teams. This is where MLOps (Machine Learning Operations) comes in. MLOps combines the principles of DevOps ...
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📚 AI News & Trends
Machine Learning models are powerful but building them is only half the story. The true challenge lies in deploying, scaling and maintaining these models in production environments – a process that requires collaboration between data scientists, developers and operations teams. This is where MLOps (Machine Learning Operations) comes in. MLOps combines the principles of DevOps ...
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