Browse Lessons

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

Avoiding buzzwords when hiring for data scientists

DevOps Vs MLOps

Different mentalities between ML engineers and Data Scientists

Evolution of data scientist titles and roles

Qualities to look for in an ML engineer

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

Why Kubeflow gained so much traction=open community

Are Kubeflow and Airflow complementary?

MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0