Beginner Lessons

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

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

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

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

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

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

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

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

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

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

MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline

3rd wave of data scientists

Reproducability flaws in end to end Machine Learning debugging

Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali

Adjacent usecases and multistep feature engineering

Checkpointing, metadata, and confidence in your data

Deeper thinking from data scientists around platform blackholes

How many models in prod til I need a dedicated ML platform?

More difficult transition for data scientists to become ML engineers

Speed, Trust, Evolution and Scale in MLOps

Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data

Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning

Resume driven development in Machine learning & software engineering

Swiss Cheese model in Machine Learning

Learning from real life Machine Learning failures

Current State Of Machine Learning

MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali

MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey

Provenance and Reproducibility in Machine Learning; what is it and why you need it?

Bare necessities for getting an ML model into production

Hierarchy of MLOps Needs

Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3

What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2