PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
*A GPU-based cloud instance will be provided to each attendee as part of this event
We will each build an end-to-end, continuous Tensorflow AI model training and deployment pipeline on our own GPU-based cloud instance.
At the end, we will combine our cloud instances to create the LARGEST Distributed Tensorflow AI Training and Serving Cluster in the WORLD!
Storing and Serving Models with HDFS
Trade-offs of CPU vs. *GPU, Scale Up vs. Scale Out
CUDA + cuDNN GPU Development Overview
Tensorflow Model Checkpointing, Saving, Exporting, and Importing
Distributed Tensorflow AI Model Training (Distributed Tensorflow)
Centralized Logging and Visualizing of Distributed Tensorflow Training (Tensorboard)
Distributed Tensorflow AI Model Serving/Predicting (Tensorflow Serving)
Centralized Logging and Metrics Collection (Prometheus, Grafana)
Continuous Tensorflow AI Model Deployment (Tensorflow, Airflow)
Hybrid Cross-Cloud and On-Premise Deployments (Kubernetes)
High-Performance and Fault-Tolerant Microsservices using Request Batching and Circuit Breakers (NetflixOSS)