Browse Lessons

Beyond the Hype: Monitoring LLMs in Production // Claire Longo // LLMs in Prod Conference Part 2

Building Production Copilots // Tristan Zajonc // LLMs in Prod Conference Part 2

Build and Customize LLMs in Less than 10 Lines of YAML // Travis Addair // LLMs in Prod Con Part 2

Embeddings and Retrieval for LLMs: Techniques and Challenges // Anton Troynikov // LLMs in Prod Con

Open Source and Fast Decision Making // Rob Hirschfeld // MLOps Podcast #164

Pitfalls and Best Practices — 5 lessons from LLMs in Production // Raza Habib // LLMs in Prod Con 2

Taking ImgFlip's 'This Meme Does Not Exist' to the Next Level with a LLM // Stefan Ojanen // LLMs

Scalable Evaluation and Serving of Open Source LLMs // Waleed Kadous // LLMs in Prod Conference

Using Vector Databases: Practical Advice for Production // Sam Partee // LLMs in Prod Conference

LLMs as Intelligent Assistants // Sarah Aerni // LLMs in Prod Conference Part 2

Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference

Building LLM Applications for Production // Chip Huyen // LLMs in Prod Conference

The Emerging Toolkit for Reliable, High-quality LLM Applications // Matei Zaharia //LLMs in Prod Con

Exploring the Challenges and Potential of LLMs // Yujian Tang // MLOps Podcast #163 short clip

Democratizing AI // Yujian Tang // MLOps Podcast #163

From Arduinos to LLMs: Exploring the Spectrum of ML // Soham Chatterjee // MLOps Podcast #162

Shopify's GPT Integration

Dataframes Are All You Need: MLOps on Easy Mode // Jay Chia // MLOps Meetup #124

Leveraging Models Without Extensive Technical Know-How // Tuhin Srivastava //MLOps Podcast #161 clip

LLMs in Production Conference - Part II

Bridging the Gap: MLOps Transformations in Streamlining Model Deployment

The Long Tail of ML Deployment // Tuhin Srivastava // MLOps Podcast #161

Balancing Data Sharing Concerns: Code as the Bridge

Assessing ML Test Scores: Surveys, Interviews, and Documentation Analysis

Expanding Capabilities and Ethical Considerations of AI Models

Nurturing Effective Search Skills for Engineers in PyTorch

Importance of Clean Code // Matt Sharp // MLOps Podcast #160 short clip

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