Intermediate Lessons

Why We Need More Data Science Podcasts

We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // Podcast #273

AI Agents in Production [Behind The Scenes]

How Memory Management Can Make Or Break Your ML Model

LLMs as a Judge?

Context Windows Are About to Level Up 🔮

Bridging the Gap Software Meets Data Science

How to Scale Your MLOps: A Comparison of Strategies

Do You Really Need a Feature Store

Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // MLOps Podcast #269

MLOps Across Borders // Gottam Sai Bhrath & Cole Bailey // MLOps Podcast #263 clip

How Agentic Workflows Will Change Everything // Raj Rikhy // MLOps Podcast #268

How to Use Differential Privacy in Your AI Prompts

How to Make Your Data Science Reproducible (and Why You Should Care)

The Future of Software Architecture for GenAI: Real-Time Data Streaming

How Data Capture Transforms ML Observability

Real-Time Event Processing for AI/ML with Numaflow // Sri Harsha Yayi // DE4AI

Engineering Your AI Platform // Panel // DE4AI

The Only Constant is (Data) Change // Panel // DE4AI

Putting the AI back in Medallion Lake Design // Simon Whiteley

How To Cut Your Data Infrastructure Costs in Half

From Notebook to Kubernetes: Scaling GenAI Pipelines with ZenML // Alex Strick van Linschoten

DuckDB is fast for analytics, but what can it do for AI? // Mehdi Ouazza // DE4AI

Building a Data Infrastructure for AI/ML

How Feature Stores Work // Simba Khadder // DE4AI

Building Hyper-Personalized LLM Applications with Rich Contextual Data // Mike Del Balso // DE4AI

Centralized or Decentralized ML Platform? // Jelmer Borst and Daniela Solis // MLOps Podcast #267

Making Your Company LLM-native // Francisco Ingham // MLOps Podcast #266

Data Quality and Tonic Textual // Adam Kamor // MLOps Podcast #262 clip

RAG Quality Starts with Data Quality // Adam Kamor // MLOps Podcast #262

Critical Importance of Vulnerability Scanning in Software Development // Harcharan Kabbay // shorts

The Future of Multimodal Models in Technology // Markus Stoll // MLOps Podcast #258 clip

Who's MLOps for Anyway? // Jonathan Rioux // MLOps Podcast #261

Alignment is Real // Shiva Bhattacharjee // MLOps Podcast #260

Visualize - Bringing Structure to Unstructured Data // Markus Stoll // MLOps Podcast #258

MLSecOps is Fundamental to Robust AISPM // Sean Morgan // MLOps Podcast #257

Ensuring Robust AI Architectures // Harcharan Kabbay // MLOps Podcast #256 clip

BigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255

ROI and Tool Transition in MLOps // Andy McMahon // MLOps Podcast #254 clip

Exploring Markovian Models // Yuri Plotkin // MLOps Podcast #253 clip

Design and Development Principles for LLMOps // Andy McMahon // MLOps Podcast #254

The Secrets of Jailbreaking LLMs // Ron Heichman // MLOps Podcast #252 clip

Building Creative and Productive Teams // Eric Landry // MLOps Podcast #249 clip

Harnessing AI APIs for Safer, Accurate, & Reliable Applications // Ron Heichman //MLOps Podcast #252

Reliable LLM Products, Fueled by Feedback // Chinar Movsisyan // MLOps Podcast #251

AI Operations Without Fundamental Engineering Discipline // Nikhil Suresh // MLOps Podcast #250

AI in Healthcare // Eric Landry // MLOps Podcast #249

Extending AI: From Industry to Innovation // Sophia Rowland & David Weik // MLOps Podcast #247