📰 AI News & Updates

Experiment Tracking in the Age of LLMs // Piotr Niedźwiedź // MLOps Podcast #168

Benchmarking LLM performance with LangChain Auto-Evaluator // Lance Martin //LLMs in Prod Con Part 2

Foundation Models in the Modern Data Stack // Ines Chami // LLMs in Prod Conference Part 2

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

The Future of Scalability in Machine Learning: Predictions and Challenges

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

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

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

Balancing Technical Debt and Future Thinking in Product Development

Improving User Experience with YouChat Conversational Interface

Airflow Sucks for MLOps // Stephen Bailey // MLOps Podcast #141

The Value of Communication in the Age of Machine Learning

Differences in Challenges Faced by Staff Engineers in Startups and Large Organizations

Rewarding Behavior with Machine Learning // Niall Murphy // MLOps Podcast #127 reels

The Power of Representation in Stakeholder Conversations // Shalabh Chaudri // MLOps Pod #146

High Precision Rules and Machine Learning in Cybersecurity // Jeremy Thomas Jordan // MLOps Shorts

Exploring the Advancements and Potential of Machine Learning

ML Ops Market: Slow and Steady Growth Amid Plateau Hype

Verticalization and Infrastructure Solutions in Machine Learning

Balancing Technical Expertise and Motivation in ML Interviews

Balancing Opinions and Data in Tool-Focused Companies #shorts

Building an Open Source MLOps Stack with ZenML Part 2 // Hamza Tahir // MLOps Meetup # 116

Real-time Machine Learning with Chip Huyen // MLOps Coffee Sessions #133

Reliable Machine Learning // Niall Murphy & Todd Underwood // Coffee Sessions #127

Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125

RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day // Gleb Abroskin // MLOps Coffee Ses #123

Scaling Similarity Learning at Digits // Hannes Hapke // Coffee Sessions #122

Feathr: LinkedIn's High-performance Feature Store // David Stein // Coffee Sessions #120

Data Engineering for ML // Chad Sanderson // Coffee Sessions #117

Scaling Machine Learning with Data Mesh // Shawn Kyzer // Coffee Sessions #116

How Hera is an Enabler of MLOps Integrations // Flaviu Vadan // Coffee Sessions #115

Just Fetch the Data and then... // David Bayliss // Coffee Sessions #110

ML Flow vs Kubeflow 2022 // Byron Allen // Coffee Sessions #108

The Post Modern Stack // Jacopo Tagliabue // MLOps Meetup #103

MLOps Critiques // Matthijs Brouns // MLOps Coffee Sessions #100

Scaling Real-time Machine Learning at Chime // Peeyush Agarwal // Lightning Sessions #1

MLOps EngineeringLabs // Niels Bantilan and Haytham Abuelfutuh // MLOps Meetup #98

MLOps as Tool to Shape Team and Culture // Ciro Greco // MLOps Coffee Sessions #95

Don't Listen Unless You Are Going to Do ML in Production // Kyle Morris // MLOps Coffee Sessions #87

Building ML/Data Platform on Top of Kubernetes // Julien Bisconti // MLOps Coffee Sessions #86

Data Mesh - Data Quality Control Mechanism for MLOps? // Scott Hirleman // MLOps Coffee Sessions #77

Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75

Scaling Biotech // Jesse Johnson // MLOps Coffee Sessions #74

Self-Assembling Machine Learning Environment (SAME) // David Aronchick // Meetup #73 short clip

Doing MLOps // Noah Gift // MLOps Meetup #80

Machine Learning SRE // Niall Murphy // MLOps Coffee Sessions #54

The SAME Project: A Cloud Native Approach to Reproducible ML // David Aronchick // MLOps Meetup #73

Orchestrating Spark Jobs with Kubeflow // Sadik Bakiu // MLOps Meetup #71