MDLI Ops 2022: slides

Recently we held the first MDLI Ops conference, an event dedicated to ML Ops.

A number of leading voices in the industry participated in our conference and shared their insights with us, and were joined by over 1,900 registrants.

We'd like to thank all the great speakers who participated, and thank our sponsors – Weights & Biases, Iguazio, Run:AI, Aporia, Superwise, Outbrain, AWS and Comet. It was great to collaborate with you!

Most importantly, thank all of you who joined us! We hope to see you next year.

Here you can find the slides from the conference and on our YouTube channel you can find the recording of the event. If you don't want to miss any updates in the future, please subscribe to our newsletter.

Our next  MDLIops 2024 event is here! For registering and watch the full agenda click here. 

The full conference playlist:

Where's the pull request for MLOps?

Chris Van Pelt, Co-founder of Weights & Biases

MLOps Beyond Training: Simplifying and Automating the Operational Pipeline

Yaron Haviv, Co-Founder & CTO of Iguazio

Building an ML Platform from Scratch

Alon Gubkin, Co-Founder & CTO of Aporia

Inference Workloads: Solving Challenges Beyond Training Models

Guy Salton, Solutions Architect at Run:AI

Implementing MLOps best practices with Amazon SageMaker

Gili Nachum, AI/ML Sr. Solutions Architect at AWS

Reproducible Machine Learning

Scott Condron, ML Engineer at W&B

Building a World-Class Supercomputer

Gilad Shainer, SVP of HPC and Networking at Nvidia

The MLOps Stack: Where do we go from here?

Moderator: Oren Razon, Co-founder and CEO of Superwise.ai

Speakers: Naama Ziporin, AutoML Team Leader at Outbrain; Or Hiltch, VP of Engineering at JLL

Offline Model Validation 101: Testing Your Model Before Deployment

Shir Chorev, Co-Founder & CTO of Deepchecks

Developing & Handling a ML algorithm in a non-technical environment

Michael Winer, Data Scientist at AppsFlyer

Feature Stores – Unified Data Pipelines for ML

Ran Romano, Co-Founder and VP Engineering at Qwak

Building a modern DS work environment with Kubernetes

Lidiya Norman, Senior Data Scientist at Big Panda; Nadav Bar-Uryan, Machine Learning Software Engineer at Big Panda

Are ML Engineers real?

Yoav Ramon, AI Lead at Hi Auto