TFX: Production ML pipelines with TensorFlow
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 explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.
|Talk Title||TFX: Production ML pipelines with TensorFlow|
|Speakers||Robert Crowe (Google)|
|Conference||O’Reilly Artificial Intelligence Conference|
|Conf Tag||Put AI to Work|
|Location||San Jose, California|
|Date||September 10-12, 2019|
Putting machine learning models into production is now mission critical for every business—no matter what size. TensorFlow is the industry-leading platform for developing, modeling, and serving deep learning solutions. But putting together a complete pipeline for deploying and maintaining a production application of AI and deep learning is much more than training a model. Google has taken its years of experience in developing production ML pipelines and offered the open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally. Robert Crowe walks you through working code in an example pipeline so you can learn what’s involved in creating a production pipeline. You’ll be able take what you learn and get started on creating your own pipelines for your applications.