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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But... show more

DeepLearning.AI

350,684 subscribers

40,878 views • 1 year ago •via X (Twitter)

Science & Technology News & Politics Education

Anya Rossi• Live Now

Private livecam show

8 Comments

Bharat Singh1 year ago

@Snowflake @datta_cs @_jreini This is a fantastic and much-needed course. Lots of tutorials cover building agents, but the "and Evaluating" part is the real gold here. Reliable evaluation for complex, multi-step agent workflows is one of the biggest challenges in MLOps right now. #AI #AIAgents #MLOps

Min Chon Chi1 year ago

@Snowflake @datta_cs @_jreini Great collaboration with Snowflake! The course description is clear and concise.

VibeCodeTeddy1 year ago

@Snowflake @datta_cs @_jreini Great focus on evaluation. It really is the missing piece in building reliable data agents. What insights or metrics are you prioritizing in the course?

Anurag Pant1 year ago

@Snowflake @datta_cs @_jreini 👍👍👍

Sinjun1 year ago

@Snowflake @datta_cs @_jreini 👏

The{CodingGuy}1 year ago

@Snowflake @datta_cs @_jreini This is a game-changer! Finally, a hands-on course to master reliable multi-step data agents , can’t wait to dive in and level up my engineering workflows.

Maku Gideon1 year ago

@Snowflake @datta_cs @_jreini Great collab

passingyears1 year ago

@Snowflake @datta_cs @_jreini the replan node in L2.ipynb should reset the current_step to 1, or the execute node will ignore first item of new plan.

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Andrew Ng

65,007 views • 10 months ago

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,894 views • 1 year ago

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

Big Pharmai

58,640 views • 1 year ago

With the launch of GLM-5, introduces Agent Mode. - Agent Mode: Automatically breaks down tasks, orchestrates tools, drives execution, and delivers ready-to-use files. - Data Insights & Smart Writing: Upload data for instant visualizations. Go from outline to finished draft, all in one place. - AI Slides / Full-Stack upgraded: Now handles more complex instructions and multi-step workflows.

With the launch of GLM-5, introduces Agent Mode. - Agent Mode: Automatically breaks down tasks, orchestrates tools, drives execution, and delivers ready-to-use files. - Data Insights & Smart Writing: Upload data for instant visualizations. Go from outline to finished draft, all in one place. - AI Slides / Full-Stack upgraded: Now handles more complex instructions and multi-step workflows.

Z.ai

30,287 views • 8 months ago