Session Topics
Cover Image: Chaos and Pain in Machine Learning, and the ‘DevOps for ML Manifesto'
Chaos and Pain in Machine Learning, and the ‘DevOps for ML Manifesto’
Perspective
Most AI/ML projects start shipping models into production, where they...
Cover Image: Culture & Organization for Effective ML at Scale
Culture & Organization for Effective ML at Scale
Panel Discussion
Operationalizing machine learning in an organization isn't only an issue...
Cover Image: Embeddings @ Twitter
Embeddings @ Twitter
Case Study
ML modeling teams at Twitter face a variety of uniquely...
Cover Image: Ethical Human-in-the-Loop ML Pipelines
Ethical Human-in-the-Loop ML Pipelines
Perspective
This talk will focus on methods that ensure the fair...
Cover Image: Exploring Tradeoffs in Experiment Management as part of AI Platforms
Exploring Tradeoffs in Experiment Management as part of AI Platforms
Technology
As teams scale their AI platforms, they must decide which...
Cover Image: Foundations for ML at Scale: Where to Start in Your Organization
Foundations for ML at Scale: Where to Start in Your Organization
Perspective
How do you set up machine learning projects to be...
Cover Image: Increasing the Impact of AI Through Better Software
Increasing the Impact of AI Through Better Software
Technology
The state of artificial intelligence is continuing to advance rapidly,...
Cover Image: Live from TWIMLcon! Encoding Company Culture in Applied AI Systems
Live from TWIMLcon! Encoding Company Culture in Applied AI Systems
Keynote Interview
Deepak Agarwal, vice president of artificial intelligence at LinkedIn, will...
Cover Image: Live from TWIMLcon! Operationalizing ML at Scale
Live from TWIMLcon! Operationalizing ML at Scale
Keynote Interview
What should we take away from how web giants and...
Cover Image: Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production
Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production
Keynote Interview
Andrew Ng, former Chief Scientist at Baidu and founding lead...
Cover Image: Live from TWIMLcon! Use-Case Driven ML Platforms
Live from TWIMLcon! Use-Case Driven ML Platforms
Keynote Interview
Franziska Bell, PHD leads a team of 100 data scientists...
Cover Image: Machine Learning in Production: What to do after you’ve trained the models?
Machine Learning in Production: What to do after you’ve trained the models?
Case Study
The search feature is often the first step for online...
Cover Image: MagLev: A Production-grade AI Platform Running on GPU-enabled Kubernetes Clusters
MagLev: A Production-grade AI Platform Running on GPU-enabled Kubernetes Clusters
Case Study
Data is central to training and validating every aspect of...
Cover Image: Model as a Service for Real-Time Decisioning​
Model as a Service for Real-Time Decisioning​
Case Study
Hosting models and productionizing them is a pain point. Let’s...
Cover Image: Model Versioning: Why, When, and How
Model Versioning: Why, When, and How
Technology
Models are the new code: while machine learning models are...
Cover Image: Operationalizing Machine Learning at Levi’s
Operationalizing Machine Learning at Levi’s
Case Study
In this talk, we will cover the journey we at...
Cover Image: Operationalizing Responsible AI
Operationalizing Responsible AI
Panel Discussion
This panel explores how organizations can go beyond a general...
Cover Image: Productionizing Machine Learning Models at Scale with Kubernetes
Productionizing Machine Learning Models at Scale with Kubernetes
Case Study
Productionizing machine learning models in an organization is difficult. The...
Cover Image: Removing Unfair Bias in the Machine Learning Pipeline
Removing Unfair Bias in the Machine Learning Pipeline
Technology
Machine learning models are increasingly being used to make critical...
Cover Image: Scaling ML in the Traditional Enterprise
Scaling ML in the Traditional Enterprise
Traditional enterprises are often burdened by factors that make scaling...
Cover Image: Semblance: Feature Generation In Real-Time and Batch Without Time-Travel
Semblance: Feature Generation In Real-Time and Batch Without Time-Travel
Technology
Introducing Semblance, a machine learning feature generation system. Semblance features...
Cover Image: SurveyMonkey's ML Platform Journey
SurveyMonkey’s ML Platform Journey
Case Study
In this talk, we will share how SurveyMonkey was able...
Cover Image: Team Teardown: Evolving an ML Platform at SurveyMonkey
Team Teardown: Evolving an ML Platform at SurveyMonkey
Panel Discussion
We typically hear conference presentations from the single perspective of...
Cover Image: Team Teardown: Organizing and Supporting ML at Airbnb
Team Teardown: Organizing and Supporting ML at Airbnb
Panel Discussion
In this panel, we will explore how teams at Airbnb...
Cover Image: Why and How to Build Explainability into your ML Workflow
Why and How to Build Explainability into your ML Workflow
Technology
Building AI applications comes with significant business risks, and nearly...
Cover Image: Why is Production ML so Hard?
Why is Production ML so Hard?
Perspective
Production ML hurdles are often organization-wide hurdles. In this talk,...
Cover Image: X1 Recommendations Platform: Started from the bottom..
X1 Recommendations Platform: Started from the bottom..
Case Study
In this talk, the Xfinity X1 recommendations platform team at...