Browse Lessons

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

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

Learning from mistakes (nobody cares about your f score)

Knowing Data Engineering as a data scientist can save your company money

MLOps Meetup #16 // Venture Capital In Machine Learning Startups with John Spindler

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

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

Show your Versatility as a Data Scientist

MLOps meetup #13 // Maximizing Job Opportunities as a Data Scientist on the Market w/ Anthony Kelly

MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

Model monitoring with Fury Data Apps at Mercado Libre

ML platform Fury Data Apps at Mecado Libre

What parts of the mercado libre ML platforms are automatted

Litox View of the state of ML today

Fury Platform and Fury Data Apps at Mercado Libre

MLOps community meetup #11 // Machine Learning at Scale in Mercado Libre with Carlos de la Torre

Why do you need Automation in Machine Learning

MLOps is an Organizational Problem Not a Tech Problem

10 Years Deploying ML in the Enterprise: The Inside Scoop! // Charles Martin // MLOps meetup #9

Case Study of when things go wrong in ML

Training ML models on live datasets in a GDPR world

Monitoring for ML concept drift in a Covid Era

Have you really thought this ML platform through?

100% 0pen sourced ML pipeline \\ MLOps open sourced

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