Schedule: Production pipelines sessions

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe outlines what's involved in creating a production ML pipeline and walks you through working code. Read more.

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. Animesh Singh, Pete MacKinnon, and Tommy Li demonstrate how to run TFX in hybrid cloud environments. Read more.

Hannes Hapke leads a deep dive into deploying TensorFlow models within minutes with TensorFlow Serving and optimizing your serving infrastructure for optimal throughput. Read more.

Twitter heavily relies on Scala and the Java Virtual Machine (JVM) and contains a lot of expertise knowledge. Shajan Dasan and Briac Marcatté detail the problems Twitter has had to overcome to make its offering reliable and provide a reliable TensorFlow inference to Twitter customer teams. Read more.

In many applications where data is generated continuously, combining machine learning with streaming data is imperative to discover useful information in real time. Yong Tang explores TensorFlow I/O, which can be used to easily build a data pipeline with TensorFlow and stream frameworks such as Apache Kafka, AWS Kinesis, or Google Cloud PubSub. Read more.

Hilbert is an AI framework that works with TensorFlow Extended (TFX) at Yahoo! JAPAN, which provides AutoML to create production-level deep learning models automatically. Hilbert is currently used by over 20 services of Yahoo! JAPAN. Shin-Ichiro Okamoto details how to achieve production-level AutoML and explores service use cases at Yahoo! JAPAN. Read more.

Juntai Zheng explains how to use the MLflow open source platform to manage the model lifecycle. It supports many model flavors, such as MLeap, MLlib, scikit-learn, PyTorch, TensorFlow, and Keras, with particular focus on TensorFlow 2.0 and Keras models. Read more.