Browse Lessons

Balancing Productivity & Prevention of Harmful Content Generation // Nils Reimers //Podcast 158 clip

Boosting Databases and Language Models

Cohere Large Language Model at Scale // Nils Reimers // MLOps Podcast #158

Understanding Semantic Vectors: Extracting Embeddings and Calculating Similarity

Data Privacy and Security // LLMs in Production Conference Panel Discussion

The Future of Closed API Models in the Age of Open Source

Building Defensible Products with LLMs // Raza Habib // LLMs in Production Conference Talk

Agentic Relationship Management // Ashe Magalhaes // LLMs in Production Conference Lightning Talk

Automating Task-specific Model Application with Snorkel Flow

ML Integration Challenges // Aaron Maurer & Katrina Ni // MLOps Podcast # 157

Tools and Technologies for Machine Learning in Kubernetes Environment

MLOps Build or Buy, Startup vs. Enterprise? // Aaron Maurer & Katrina Ni // MLOps Podcast # 157

Democratization of Technology through Machine Learning and Containerization

Cost Optimization and Performance // LLMs in Production Conference Panel Discussion 2

What is the role of ML Engineers in the time of GPT4 and BARD? // Hannes Hapke // LLMs in Prod Con

From Idea to Product: The Development of Apache Spark

Generative Interfaces Beyond Chat // Linus Lee // LLMs in Production Conference

Machine Learning Education at Uber // Melissa Barr & Michael Mui // MLOps Podcast #156

Promoting Distributed Innovation // Melissa Barr & Michael Mui // MLOps Podcast #154 video clip

Building Machine Learning Workflows: Lessons Learned

Understanding User Needs in Machine Learning and AI Development

How LlamaIndex Can Bring Power of LLM's to Your Data //Jerry Liu // LLM's In Prod Lightning talk 3

Spark and MLflow Similarities // Matei Zaharia // MLOps Podcast #155 short clip

LangChain: Enabling LLMs to Use Tools // Harrison Chase // LLMs in Production Conference

Age of Industrialized AI // Dan Jeffries // LLMs in Production Conference

DevTools for Language Models: Unlocking the Future of AI-Driven Applications // Diego Oppenheimer

The Future of Scalability in Machine Learning: Predictions and Challenges

The Birth and Growth of Spark: An Open Source Success Story // Matei Zaharia // MLOps Podcast #155

Deep Learning in Google Maps

Scaling Algorithms with Machine Learning: A Google Engineer's Journey

Vector Databases and Large Language Models // Samuel Partee // LLMs in Production Conference

Challenges and Opportunities in Building Data Science Solutions with LLMs // QuantumBlack Roundtable

Want High Performing LLMs? Hint: It is All About Your Data // Vikram Chatterji // LLMs in Prod Con

Solving the Last Mile Problem of Foundation Models with Data-Centric AI //Alex Ratner // LLM in Prod

Declarative MLOps - Streamlining Model Serving on Kubernetes // Rahul Parundekar// MLOps Meetup #123

ML Scalability Challenges // Waleed Kadous // MLOps Podcast # 154

ML Evolution & Recurring Themes // Waleed Kadous // MLOps Podcast # 155 short clip

Formula for Successful Jupyter Notebook Deployment // Keith Trnka // MLOps Podcast #148 short clip

Tecton 0.6: Notebook-driven Development // Jason Dunne // MLOps Meetup #122

Code Modularization Techniques // Rodolfo Núñez // MLOps Podcast #153 short clip

Multilingual Programming and a Project Structure to Enable It // Rodolfo Núñez // MLOps Podcast #153

The Future of Data Science Teams: A World Without Data Teams?

Balancing Technical Debt and Future Thinking in Product Development

The Challenge of Dynamic AI Models in the Real World

Enabling Rapid Updates of Models: Parallels with Software and Security

Improving User Experience with YouChat Conversational Interface

How A Manager Became a Believer in DevOps for Machine Learning // Keith Trnka // MLOps Podcast #152

Addressing Latency Challenges in Large Language Models