MLOps World: Machine Learning in Production

Building the NeurIPS LLM Efficiency Challenge Leaderboard

LLMs from Hallucinations to Relevant Responses

How to Finetune Your LLMs and Evaluate Performance

Operationalizing Data-Centric AI: Practical Tools to Quickly Improve ML Datasets

LLMs, Big Data, and Audio: Breaching an untapped Gold Mine

Overcoming the Challenges of Building GenAI Apps

Training LLMs: Lessons from the Trenches

How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

Hyper-Scaling Real-Time Personalization with Privacy via On-Device Computing

Your AI Applications Need Guardrails: Here's How To Build Them

Building Your LLM Factory

Workshop Sessions: Hands on Workshop - How to Build Pipelines with Kubeflow

Iterative development workflows for building AI applications

Systematic Approaches and Creativity; Building DoorDash's ML Platform During the Pandemic

Workshop Sessions: Productionize with Kubeflow Orchestration with Feast in GCP

Workshop Sessions: Complete ML Lifecycle with MLflow - Learn it's Four Components

Querying and Seeing Time Series Data within a Self-Organizing Mesh Network

Scalable monitoring of ML models in production environments

Developing a Data-Centric NLP Machine Learning Pipeline

Introduction to Drifter ML

Security Audits for Machine Learning Attacks

Workshop Sessions: Fixing Data Quality at Scale with Data Observability

Model Monitoring: What, Why, and How

Managing Machine Learning Experiments with MLflow

Applying Responsible AI with the Open-Source LangTest Library

Workshop Sessions: Closing the Production Gap with MLOps

Workshop Sessions: Good, Fast, Cheap - How to Do Data Science with Missing Data

MemeGPT: Creating a Large Language Model to Generate Memes

DVC: Data Versioning and ML Experiments on Top of Git

Scaling AI in Production with PyTorch

Multi-armed Bandits in Production at Stitch Fix

Integrating multiple MLOps tools together on Google Cloud Platform

Workshop Sessions: Building an ML Platform from Scratch

MLOps Platform Architecture for e2e ML Pipelines

Workshop Sessions: Deploying an E2E ML Pipeline with AWS SageMaker - What Amazon didn't tell you

Lessons Learned Building Natural Language Processing Systems with David Talby

Shopping Recommendations at Pinterest

Workshop Sessions: Part 1 From Concept to Production - Template for the Entire ML Journey

Smart Data Products From prototype to production

Learn Your Codebase: Fine-tuning CodeLlama with Flyte… to Learn Flyte

Machine Learning Simplified from Ideation to Deployment in Minutes with Automated Machine Learning