talk-data.com

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

This meetup is in association with IBM. ----------------------------------------- Note: PyData Ireland will be collecting your name and email for smooth access to the venue. ----------------------------------------- APIs changed the web. MCP (Model Context Protocol) is changing AI. In this deep dive, Mihai takes you under the hood of the new interoperability layer that's powering 15,000+ AI tools and servers across the ecosystem.This session focuses on building real, working MCP servers in Python and understanding how they connect through ContextForge: https://github.com/IBM/mcp-context-forge - an open source MCP Gateway and Registry that serves as a central hub for tools\, resources\, and prompts accessible to MCP-compatible LLMs. ContextForge converts REST APIs to MCP\, composes virtual MCP servers with added security and observability\, and bridges protocols such as stdio\, SSE\, and Streamable HTTP.

Key Takeaways: Build secure, scalable MCP Servers to drive AI Agents. See how MCP and ContextForge work together to make AI tools interoperable, secure, and production-ready.

Speaker Designation: Mihai Criveti, Distinguished Engineer for AI Agents at IBM

Speaker Bio: Mihai Criveti is a Distinguished Engineer for AI Agents at IBM and leading the development of ContexForge - the open source Model Context Protocol (MCP) Gateway and Registry. He shapes Agentic AI standards across IBM and is building a team in Dublin to advance ContextForge and its global adoption. His work focuses on platform engineering, AI orchestration, and open systems that accelerate real-world AI adoption. --------------------------------------------

P.S: ContextForge team is hiring. Read on and apply.

From team ContextForge: We're looking for highly motivated Python Developers - from early-career engineers to senior contributors — to join the ContextForge MCP & A2A Gateway team at IBM Software. ContextForge - https://github.com/IBM/mcp-context-forge is an open-source, production-grade gateway, proxy, and registry for Model Context Protocol (MCP) servers and A2A Agents, unifying discovery, authentication, rate-limiting, observability, and federation across distributed AI and REST ecosystems

Application portal: https://ibmglobal.avature.net/en_US/careers/JobDetail?jobId=71800

GitHub - IBM/mcp-context-forge: 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).

Mention in the application #pydata

In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly.

We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows.

This session is a part of a series! To learn more, click here

Pre-requisites: If you'd like to follow along with the live examples, make sure you've got a GitHub account.

Habla español? Tendremos una serie para hispanohablantes!

In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly.

We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows.

This session is a part of a series! To learn more, click here

Pre-requisites: If you'd like to follow along with the live examples, make sure you've got a GitHub account.

Habla español? Tendremos una serie para hispanohablantes!

In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly.

We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows.

This session is a part of a series! To learn more, click here

Pre-requisites: If you'd like to follow along with the live examples, make sure you've got a GitHub account.

Habla español? Tendremos una serie para hispanohablantes!

Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his journey from initial skepticism to embracing agentic AI as model and application advancements made it practical for governed workflows, and explores how Compass redefines the relationship between data teams and stakeholders by shifting analysts into steward roles, capturing and governing context, and integrating with Slack where collaboration already happens. The conversation covers organizational observability through Compass's conversational system of record, cost control strategies, and the implications of agentic collaboration on Conway's Law, as well as what's next for Compass and Nick's optimistic views on AI-accelerated software engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Nick Schrock about building an AI analyst that keeps data teams in the loopInterview IntroductionHow did you get involved in the area of data management?Can you describe what Compass is and the story behind it?context repository structurehow to keep it relevant/avoid sprawl/duplicationproviding guardrailshow does a tool like Compass help provide feedback/insights back to the data teams?preparing the data warehouse for effective introspection by the AILLM selectioncost managementcaching/materializing ad-hoc queriesWhy Slack and enterprise chat are important to b2b softwareHow AI is changing stakeholder relationshipsHow not to overpromise AI capabilities How does Compass relate to BI?How does Compass relate to Dagster and Data Infrastructure?What are the most interesting, innovative, or unexpected ways that you have seen Compass used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Compass?When is Compass the wrong choice?What do you have planned for the future of Compass?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links DagsterDagster LabsDagster PlusDagster CompassChris Bergh DataOps EpisodeRise of Medium Code blog postContext EngineeringData StewardInformation ArchitectureConway's LawTemporal durable execution frameworkThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Most teams end up in this situation: ship a feature to 10% of users, wait a week, check three different tools, try to correlate the data, and you’re still unsure if it worked. The problem is that each tool has its own user identification and segmentation logic. Statsig solved this problem by building everything within a unified platform. Check out Statsig. •⁠ Linear – The system for modern product development. In the episode, Armin talks about how he uses an army of “AI interns” at his startup. With Linear, you can easily do the same: Linear’s Cursor integration lets you add Cursor as an agent to your workspace. This agent then works alongside you and your team to make code changes or answer questions. You’ve got to try it out: give Linear a spin and see how it integrates with Cursor. — Armin Ronacher is the creator of the Flask framework for Python, was one of the first engineers hired at Sentry, and now the co-founder of a new startup. He has spent his career thinking deeply about how tools shape the way we build software. In this episode of The Pragmatic Engineer Podcast, he joins me to talk about how programming languages compare, why Rust may not be ideal for early-stage startups, and how AI tools are transforming the way engineers work. Armin shares his view on what continues to make certain languages worth learning, and how agentic coding is driving people to work more, sometimes to their own detriment.  We also discuss:  • Why the Python 2 to 3 migration was more challenging than expected • How Python, Go, Rust, and TypeScript stack up for different kinds of work  • How AI tools are changing the need for unified codebases • What Armin learned about error handling from his time at Sentry • And much more  Jump to interesting parts: • (06:53) How Python, Go, and Rust stack up and when to use each one • (30:08) Why Armin has changed his mind about AI tools • (50:32) How important are language choices from an error-handling perspective? — Timestamps (00:00) Intro (01:34) Why the Python 2 to 3 migration created so many challenges (06:53) How Python, Go, and Rust stack up and when to use each one (08:35) The friction points that make Rust a bad fit for startups (12:28) How Armin thinks about choosing a language for building a startup (22:33) How AI is impacting the need for unified code bases (24:19) The use cases where AI coding tools excel  (30:08) Why Armin has changed his mind about AI tools (38:04) Why different programming languages still matter but may not in an AI-driven future (42:13) Why agentic coding is driving people to work more and why that’s not always good (47:41) Armin’s error-handling takeaways from working at Sentry  (50:32) How important is language choice from an error-handling perspective (56:02) Why the current SDLC still doesn’t prioritize error handling  (1:04:18) The challenges language designers face  (1:05:40) What Armin learned from working in startups and who thrives in that environment (1:11:39) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode:

— Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Summary In this episode of the AI Engineering Podcast Mark Brooker, VP and Distinguished Engineer at AWS, talks about how agentic workflows are transforming database usage and infrastructure design. He discusses the evolving role of data in AI systems, from traditional models to more modern approaches like vectors, RAG, and relational databases. Mark explains why agents require serverless, elastic, and operationally simple databases, and how AWS solutions like Aurora and DSQL address these needs with features such as rapid provisioning, automated patching, geodistribution, and spiky usage. The conversation covers topics including tool calling, improved model capabilities, state in agents versus stateless LLM calls, and the role of Lambda and AgentCore for long-running, session-isolated agents. Mark also touches on the shift from local MCP tools to secure, remote endpoints, the rise of object storage as a durable backplane, and the need for better identity and authorization models. The episode highlights real-world patterns like agent-driven SQL fuzzing and plan analysis, while identifying gaps in simplifying data access, hardening ops for autonomous systems, and evolving serverless database ergonomics to keep pace with agentic development.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Marc Brooker about the impact of agentic workflows on database usage patterns and how they change the architectural requirements for databasesInterview IntroductionHow did you get involved in the area of data management?Can you describe what the role of the database is in agentic workflows?There are numerous types of databases, with relational being the most prevalent. How does the type and purpose of an agent inform the type of database that should be used?Anecdotally I have heard about how agentic workloads have become the predominant "customers" of services like Neon and Fly.io. How would you characterize the different patterns of scale for agentic AI applications? (e.g. proliferation of agents, monolithic agents, multi-agent, etc.)What are some of the most significant impacts on workload and access patterns for data storage and retrieval that agents introduce?What are the categorical differences in that behavior as compared to programmatic/automated systems?You have spent a substantial amount of time on Lambda at AWS. Given that LLMs are effectively stateless, how does the added ephemerality of serverless functions impact design and performance considerations around having to "re-hydrate" context when interacting with agents?What are the most interesting, innovative, or unexpected ways that you have seen serverless and database systems used for agentic workloads?What are the most interesting, unexpected, or challenging lessons that you have learned while working on technologies that are supporting agentic applications?Contact Info BlogLinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links AWS Aurora DSQLAWS LambdaThree Tier ArchitectureVector DatabaseGraph DatabaseRelational DatabaseVector EmbeddingRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeGraphRAGAI Engineering Podcast EpisodeLLM Tool CallingMCP == Model Context ProtocolA2A == Agent 2 Agent ProtocolAWS Bedrock AgentCoreStrandsLangChainKiroThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Let's discuss Python and Agentic AI and highlighting its transformative potential compared to traditional AI. Plus we will discuss other topics you're burning to talk about.

Please be advised that this is a tight space so there may be an attendee limit and possibly a waiting list If there are a lot of RSVP's. I'll keep you updated.

The Meetup will officially end at 8:00, but you can stay until closing which is 9pm.

PARKING:

PARK AT THE LOT AT THE CORNER OF W 25TH ST AND CHURCH AVE (UNDER THE MURAL), or on the street.

If you RSVP that you'll attend but will be late, get lost, can't find us or can't make it PLEASE call or text me at 216-8709436