Intermediate Lessons

Continuous Delivery for Machine Learning article by Martin Fowler // Coffee Session short clip #53

Data Quality Test // David Aponte // Coffee Session short clip #53

Durable Data Discovery: Making Exploratory Analysis Stick // James Campbell // MLOps Meetup #86

PyTorch: Bridging AI Research and Production // Dmytro Dzhulgakov // MLOps Coffee Sessions #63

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

System Design for Recommendations and Search // Eugene Yan // MLOps Meetup #78

Make deep learning more accessible // Jeremy Howard // Coffee Sessions #47 short clip

Maintainability in Diversity // Pablo Estevez // Coffee Session #46 short clip

Three Blocks of MLOps Modular Level // Emmanuel Raj // Meetup #69 short clip

Sri's Favorite Model Release Strategy // Srivatsan Srinivasan //MLOps Coffee Sessions #40 short clip

Educating Clients on Uncertainty // Adam Sroka // MLOps Coffee Sessions #38 short clip

Nick's Nightmarish Story // Nick Masca // Coffee Session #35 short clip

Scaling AI in Production // Srivatsan Srinivasan // MLOps Coffee Sessions #40

Model & Data Drift Deep Dive // Ben Wilson // MLOps Meetup Clips

Creator of Kubeflow's opinion on a feature store // MLOps Meetup Clips

Start-ups vs Enterprise OSS Strategy in Machine Learning Tooling // MLOps Clips with Sarah Catanzaro

Model Watching: Keeping Your Project in Production // Ben Wilson // MLOps Meetup #58

The Power of Explainable AI // Krishna Gade // MLOps Meetup #53 short clip

Machine Learning Design Patterns for MLOps // Valliappa Lakshmanan // MLOps Meetup #49

When to Build and When to Buy // Nathan Benaich // MLOps Meetup #7

MLOps Matures With Companies Doing ML

Analyzing “Continuous Delivery and Automation Pipelines in ML" Part 3 // MLOps Coffee sessions #11

MLOps Meetup #35 // Bring Your On-Prem ML Use Cases to Production on Google Cloud using Kubeflow

MLOps #34: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent

MLOps #33 Owned By Statistics: How Kubeflow & MLOps Can Help Secure ML Workloads // David Aronchick

MLOps Cubonacci workshop

MLOps Day Conference - We Sponsored a Conference!

MLOps Meetup #26 / How to Leverage ML Tooling Ecosystem / Mariya Davydova Head of Product at Neu ro

MLOps #22 // Feature Stores: An Essential Part of the ML Stack to Build Great Data // Kevin Stumpf

Monitoring the Machine Learning Stack // Lina Weichbrodt // MLOps Meetup #23

MLOps Meetup #20 Build vs Buy an ML platform // Diego Oppenheimer - CEO Algorithmia

Building an ML platform is a journey that's constantly evolving

Mlflow Open source framework Hermoine demo

MLOps Common Mistakes why Hermione was Born

What is the open source ML framework Hermoine

MLOps #17 // The Challenges of ML Operations & How Hermione Helps Along the Way // Neylson Crepalde

MLOps Meetup #15 // Scaling Human-in-the-Loop Machine Learning with Robert Munro

MLOps meetup #14 // Kubeflow vs MLflow with Byron Allen

MLOps.community #10 // MLOps - The Blind Men and the Elephant with Saurav Chakravorty

Machine Learning and an Arrikto Overview

Evolution of Kubeflow

High level overview of Kubeflow

Kubeflow mission statement breakdown

Kubeflow vs SageMaker in Machine Learning

Machine Learning Lifecycles//Perception vs Reality

Versioning your ML steps with Kubeflow

What do Kubeflow and Arrikto do and how do they work together?

Who decides the dirrection of Kubeflow