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Using Amazon SageMaker to Operationalize Machine Learning 10:30am - 11:30am
Nvidia “NGC” Deep Learning Containers 11:30am - 12:30pm
How the Experts Do it: Production ML at Scale 1:30pm - 2:30pm
Impact of Data Regulations and Bias on Operational ML 2:30pm - 3:30pm
Low-latency Job Scheduling with Preemption for t...
tensorflow-tracing: A Performance Tuning Framewo...
Fast, Reliable, Yet Catastrophically Failing!?!...
Disdat: Bundle Data Management for Machine Learn...
TonY: An Orchestrator for Distributed Machine Le...
Transfer Learning for Performance Modeling of De...
Conference Luncheon 12:30pm - 1:30pm
Continental Breakfast and Badge Pickup 8:00am - 9:00am
Break with Refreshments 10:00am - 10:30am
Break with Refreshements 3:30pm - 4:00pm
Happy Hour and Poster Session 6:00pm - 7:00pm
Opening Remarks 9:00am - 9:15am
Ray: A Distributed Framework for Emerging AI App... 9:15am - 10:00am
MLOp Lifecycle Scheme for Vision-based Inspectio...
Deep Learning Vector Search Service
Signal Fabric—An AI-assisted Platform for Knowle...
Relevance Debugging and Explaining at LinkedIn
Shooting the Moving Target: Machine Learning in...
Deep Learning Inference Service at Microsoft
Predictive Caching@Scale 1:30pm - 1:50pm
Towards Taming the Resource and Data Heterogenei...
A Comprehensive View of a Large-Scale Deep Learn...
The Power of Metrics—How to Monitor and Improve...
MPP: Model Performance Predictor
Manifold: A Model-Agnostic Visual Debugging Tool...
KnowledgeNet: Disaggregated and Distributed Trai...
Continuous Training for Production ML in the Ten...
Reinforcement Learning Based Incremental Web Cra...
Katib: A Distributed General AutoML Platform Bas...
Machine Learning Models as a Service
Stratum: A Serverless Framework for Lifecycle Ma...
Opportunities and Challenges Of Machine Learning...
Accelerating Large Scale Deep Learning Inference...
A Distributed Machine Learning For Giant Hogweed...
AIOps: Challenges and Experiences in Azure
Quasar: A High-Performance Scoring and Ranking L...
AI from Labs to Production - Challenges and Lear...
Deep Learning Lifecycle Management with Kubernetes, REST, and Py... 1:30pm - 2:30pm
ModelOps on AWS 2:30pm - 3:30pm
Accelerating the Machine Learning Lifecycle with MLflow 4:00pm - 5:00pm
Consistent Multi-Cloud AI Lifecycle Management with Kubeflow 5:00pm - 6:00pm
