Browse Lessons

MLOps for Ad Platforms // Andrew Yates // MLOps Coffee Sessions #130

Applying DevOps Practices in Data and ML Engineering // Antoni Ivanov // MLOps Meetup #111

Voice and Language Tech // Catherin Breslin // Coffee Sessions #129

Managing Machine Learning Projects // Simon Thompson // MLOps Coffee Sessions #128

Obtain New Insights on Model Behavior with Fiddler // Danny Brock // MLOps Meetup #110

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

ML Unicorn Start-up Investor Tells-IT-All // George Mathew // MLOps Coffee Sessions #126

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

Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2

Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #124

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

Bringing DevOps Agility to ML // Luis Ceze // Coffee Sessions #121

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

MLflow Pipelines: Opinionated ML Pipelines in MLflow // Xiangrui Meng // Coffee Sessions #112

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

Turning Redis into a Composable, ML Data Platform // Samuel Partee // Coffee Sessions #111

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

FLOps with Scaleout's Open-core Platform // Marco Capuccini // MLOps Meetup #104

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

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

Making MLFlow // Lead MLFlow Maintainer Corey Zumar // MLOps Coffee Sessions #103

Building a Movie Recommendation System on Tecton with Snowflake // David Hershey //MLOps Meetup #102

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