Browse Lessons

Clean Code for Data Scientists // Matt Sharp // MLOps Podcast # 160

LLMs in Production Conference Part 2

Fine-Tuning LLMs: Best Practices and When to Go Small // Mark Kim-Huang // MLOps Meetup #124

Addressing the Cold Start Problem: Heuristic Models and Training Data in Slack

Assessing and Improving MLOps Maturity // Maria Vechtomova & Basak Eskili // MLOps Podcast #159 clip

Privacy and Security in Slack: Safeguarding Data in Features and Models

Why is MLOps Hard in an Enterprise? // Maria Vechtomova & Basak Eskili // MLOps Podcast #159

Using LLMs to Punch Above your Weight! // Cameron Feenstra // LLMs in Production Conference Talk

Guiding LLMs While Staying in the Driver's Seat // Jacob van Gogh // LLMs in Prod Con Lightning talk

Reasoning Machines // Justin Uberti & Jon Turow // LLMs in Production Conference Lightning Talk 1

Ensuring Accuracy and Quality in LLM-driven Products // Adam Nolte // LLMs in Prod Conference

Emerging Patterns for LLMs in Production // Willem Pienaar // LLMs in Prod Conference Lightning Talk

No Rose Without a Thorn - Obstacles to Successful LLM Deployments // Tanmay Chopra // LLMs in Prod

Value of External Resources for Building a Company Platform

Exploring Generative AI and ChatGPT for Uber's Machine Learning Platform

Unleashing Speech Intelligence through DSLMs // Andrew Seagraves // LLMs in Prod Con Lightning Talk

LangChain Apps to Production with LangChain-serve // Deepankar Mahapatro // LLMs Con Lightning talk

The Significance of Data and Balancing Inputs for Effective Model Training

LLM Deployment with NLP Models // Meryem Arik // LLMs in Production Conference Lightning Talk 2

Efficiently Scaling and Deploying LLMs // Hanlin Tang // LLM's in Production Conference

Data-Centric AI: Unlocking the Power of Quality Data

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