Browse Lessons

SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions #23

How To Move From Barely Doing BI to Doing AI // Joe Reis // MLOps Meetup #45

Deep in the Heart of Data // Carl Steinbach // MLOps Coffee Sessions #22

Ep#7 - ML and Encryption - It's all about secure insights

Ep#6 - Privacy-preserving ML with Differential Privacy

Human-centric ML Infrastructure: A Netflix Original // Savin Goyal // MLOps Meetup #44

A Conversation with Seattle Data Guy // Benjamin Rogojan // MLOps Coffee Sessions #21

Monzo Bank - An MLOps Case Study // Neal Lathia // MLOps Coffee Sessions #20

Ep#5 - The intersection between DataOps and privacy

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

Ep#4 - Are Privacy-enchancing technologies a myth?

MLOps Matures With Companies Doing ML

Introducing Data Downtime: From Firefighting to Winning // Barr Moses // MLOps Coffee Sessions #19

The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43

Ep#3 - AI an ethical dilemmas

UN Global Platform // Mark Craddock // Co-Founder & CTO, Global Certification // MLOps Meetup #42

Ep#2 - What are regulations saying about data privacy?

Metaflow: Supercharging Our Data Scientist Productivity // Ravi Kiran Chirravuri // MLOps Meetup #41

Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production

When Machine Learning meets privacy - Episode 1

CI/CD & Continuous Training in ML // Part 4 // MLOps Coffee Sessions #17

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