Beginner Lessons

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

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

MLOps at DoorDash // Hien Luu and DoorDash Leads // Coffee Sessions #119

ML Platforms, Where to Start? // Olalekan Elesin // Coffee Sessions #118

From Expectations to Synthetic Data Generation // Fabiana Clemente // MLOps Meetup #109

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

Product Enrichment and Recommender Systems // Marc Lindner and Amr Mashlah // Coffee Sessions #114

Building Better Data Teams // Leanne Fitzpatrick // Coffee Sessions #113

Argo Workflows // Kemal Tugrul Yesilbek // MLOps Meetup #108

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

So Fresh and So Data Clean // Tommy Dang // MLOps Meetup #107

Why You Need More Than Airflow // Ketan Umare // Coffee Sessions #109

DevOps, Security, and Observability in ML // Luke Marsden // MLOps Meetup #106

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

Code Quality in Data Science // Laszlo Sragner // MLOps Meetup #105

Why and When to Use Kubeflow for MLOps // Ryan Russon // Coffee Sessions #107

Speed Up Data-Driven Value // Delina Ivanova // MLOps Coffee Sessions #106

Labeled Datasets that Correct Themselves Automatically // Curtis Northcutt // Coffee Sessions #105

MLOps + BI? // Maxime Beauchemin // MLOps Coffee Sessions #104

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

Fixing Your ML Data Blind Spots // Yash Sheth // MLOps Coffee Sessions #102

Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino

On Juggling, Dr. Seuss and Feature Stores for Real-time AI/ML // Nava Levy // MLOps Meetup #101

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

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

GPU For Machine Learning // Ronen Dar & Gijsbert Janssen van Doorn // MLOps Coffee Sessions #99

DataOps is a Software Engineering Challenge // Micha Kunze // MLOps Meetup #100

Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98

The Role of Resource Management in MLOps// Ronen Dar & Gijsbert Janssen van Doorn// MLOps Meetup #99

Real-Time Processing with Apache Flink, Kafka, and Pinot //Jacob Tsafatinos // Coffee Sessions #97

FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96

Uber’s Real-Time Ad Processing System // System Design Review

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

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

Traversing the Data Maturity Spectrum: A Startup Perspective // Mark Freeman // Coffee Sessions #94

Modern Data Science with Vaex // Maarten Breddels and Jovan Veljanoski // MLOps Meetup #97

Model Monitoring in Practice: Top Trends // Krishnaram Kenthapadi // MLOps Coffee Sessions #93

Building the World's First Data Engineering Conference // Pete Soderling //MLOps Coffee Sessions #92

The Shipyard: Lessons Learned While Building an ML Platform // Joseph Haaga // Coffee Sessions #91

Bringing Audio ML Models into Production // @ValerioVelardoTheSoundofAI //Coffee Sessions #90

A Journey in Scaling ML // Gabriel Straub // MLOps Coffee Sessions #89

ML Platform Tradeoffs and Wondering Why to Use Them // Javier Mansilla // MLOps Coffee Sessions #88

Building a Modern Data Analytics Stack // Jeff Katz // MLOps Meetup #96

Applications of Data Science // Connie Yang // MLOps Meetup #95

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

Machine Learning Engineering in Action // Ben Wilson // Reading Group #5