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 Sc...
tensorflow-tracing...
Fast, Reliable, Ye...
Disdat: Bundle Dat...
TonY: An Orchestra...
Transfer Learning...
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
Ray: A Distributed Framework for Emerging AI App... 9:15am - 10:00am
MLOp Lifecycle Sch...
Deep Learning Vect...
Signal Fabric—An A...
Relevance Debuggin...
Shooting the Movin...
Deep Learning Infe...
Predictive Caching...
Towards Taming the...
A Comprehensive Vi...
The Power of Metri...
MPP: Model Perform...
Manifold: A Model-...
KnowledgeNet: Disa...
Continuous Trainin...
Reinforcement Lear...
Katib: A Distribut...
Machine Learning M...
Stratum: A Serverl...
Opportunities and...
Accelerating Large...
A Distributed Mach...
AIOps: Challenges...
Quasar: A High-Per...
AI from Labs to Pr...
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