Browse Lessons

Hands-on Serving Models Using KFserving // Theofilos Papapanagiotou // MLOps Meetup #40

Operationalize Open Source Models with SAS Open Model Manager // Ivan Nardini // MLOps Meetup #39

Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16

Scalable Python for Everyone, Everywhere // Matthew Rocklin // MLOps Meetup #38

MLOps + Machine Learning // James Sutton // MLOps Coffee Sessions #15

MLOps Coffee Sessions #13 How to Choose the Right ML Tool // Jose Navarro and Mariya Davydova

MLOps Coffee Sessions #14 Conversation with the Creators of Dask // Hugo Bowne and Matthew Rocklin

MLOps #37 When You Say Data Scientist Do You Mean Data Engineer? Lessons Learned From Startup Life

ML obstacles for startups

Coffee sessions #12: Journey of Flyte at Lyft and Through Open-source // Ketan Umare

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

MLOps #36 Moving Deep Learning from Research to Prod Using DeterminedAI & Kubeflow // David Hershey

Most underrated topics regarding deploying ML models in production?

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

Continuous Delivery and Automation Pipelines in ML // Part 2 // MLOps Coffee Sessions #10

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

Continuous Delivery and Automation Pipelines in Machine Learning // Part 1 // MLOps Coffee Sessions

MLOps Meetup #32 // Building Say Less: An AI-Powered Summarization App // Yoav Zimmerman - Model Zoo

MLOps Coffee Sessions #8 // MLOps from the Perspective of an SRE // Neeran Gul

MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady

MLOps #31 Creating Beautiful Ambient Music with Google Brain’s Music Transformer // Daniel Jeffries

MLOps #30 // Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist

MLOps Cubonacci workshop

MLOps Meetup #29 / Scaling ML Capabilities in Large Organizations // Bertjan Broeksema & Axel Goblet

MLOps Coffee Sessions #6 // Continuous Integration for ML // Featuring Elle O'Brien

MLOps Coffee Sessions #5 // Airflow in MLOps // Featuring Simon Darr and Byron Allen

Introducing MLOps Engineering Labs - Join us to better your skills

MLOps Day Conference - We Sponsored a Conference!

MLOps #28 Continuous Evaluation & Model Experimentation // Danny Ma - Founder of Sydney Data Science

MLOps Coffee Sessions #4: A Conversation Around Feature Stores with Venkata Pingali and Jim Dowling

MLOps Meetup #27 // ML Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI

MLOps Coffee Sessions #3 // MLOps: Isn't That Just DevOps? // Featuring Ryan Dawson

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

MLOps Meetup #25 Python and Dask: Scaling the DataFrame // Dan Gerlanc - Founder of Enplus Advisors

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

MLOps Meetup #24 // How to Become a Better Data Scientist: The Definite Guide // Alexey Grigorev

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

MLOps Meetup #21 // Deep Dive on Paperspace Tooling // Misha Kutsovsky

MLOps Coffee Sessions #2: Different Ways of Serving ML Models with Byron Allen

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

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

MLOps Meetup #18 // Nubank - Running a Fintech on ML // Caique Lima and Cristiano Breuel

MLOps Meetup #19 // DataOps and Data Versioning in ML // Dmitry Petrov

MLOps Coffee Sessions #1: Serving Models with Kubeflow

Mlflow Open source framework Hermoine demo

MLOps Common Mistakes why Hermione was Born

What is the open source ML framework Hermoine