Best Practices for Enterprise-Grade MLOps and Governance with Snowflake Catch this ML expert-led session to learn the best practices for designing and managing enterprise-grade ML development and production systems at scale. You’ll learn how Snowflake ML makes it easy to rapidly develop and prototype new ML projects while ensuring production ML systems deploy and operate in a secure, governed manner. Laying the Foundation for Enterprise MLOps: Workflow Orchestration with ZenML Effective ML orchestration is the foundation of successful enterprise AI systems, connecting data processing, training, and deployment into reproducible workflows. This session explores how ZenML provides the critical pipeline infrastructure that enables teams to standardize their ML processes while maintaining flexibility. Operationalizing Data for Agents and Models with Featureform, MCP, and Iceberg For years, feature platforms like Featureform have powered classical ML systems—serving features to models, productionizing transformations, and helping ML teams scale. But the rise of LLMs and agentic workflows is fundamentally expanding the surface area of the ML platform, introducing new patterns for how data is consumed and acted on. In this session, we’ll explore the next evolution of the feature platform: one that supports both real-time and batch pipelines, bridges traditional ML and agentic systems, and makes data accessible through interfaces like MCP.

Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators.

An analysis of the April 2025 GPT-4o sycophancy incident through the lens of MLOps. Learn why prompt changes demand rigorous deployment strategies (Canary, Shadow) and how neglecting MLOps/LLMOps principles impacts AI safety and user trust in Large Language Models (LLMs) and Machine Learning systems.

Open-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure.

Existing BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data. Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability. We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data.

This article introduces BKFC (Build Knowledge From Chats), a Python notebook designed as an agentic workflow to tackle the common problem of extracting useful information from cluttered Google Chat histories. The author explains how manually searching through chats is inefficient. BKFC automates this by fetching recent messages via the Google Chat API, processing them, and then using Vertex AI's Gemini model for analysis. Crucially, it prompts Gemini to return structured insights (like summaries, Q&A, action items, project updates) based on a predefined Pydantic schema. The tool demonstrates a practical way to use AI (specifically Gen AI and APIs) to turn conversational data into organized, actionable knowledge, saving time and improving team awareness.

A discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data.

As AI moves beyond general-purpose LLMs, domain-specific agents are redefining automation, decision-making, and real-world applications. But what does this mean for MLOps, infrastructure, and AI adoption? How can open-source AI keep up with this shift?