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Chapter 12
TFX uses the open source Apache Beam to implement data-parallel pipelines.
Optionally TFX allows Apache Airflow and Kubeflow for easy configuration,
operation, monitoring, and maintenance of the ML pipeline. Once the model is
developed and trained, using TFX you can deploy it to one or more deployment
target(s) where it will receive inference requests. TFX supports deployment to
three classes of deployment targets: TensorFlow Serving (works with REST or
gRPC interface), TensorFlow.js (for browser applications), and TensorFlow Lite
(for native mobile and IoT applications). Trained models that have been exported as
SavedModels can be deployed to any or all of these deployment targets.
TensorFlow Enterprise
TensorFlow Enterprise is the latest offering from Google that provides enterprisegrade
support, cloud-scale performance, and managed services. TensorFlow
Enterprise has been launched as a beta version. Its aim is to accelerate software
development and ensure the reliability of launched AI applications. It is fully
integrated with Google Cloud and its services, and introduces some improvements
in the way TensorFlow Datasets reads data from Cloud Storage. TensorFlow
Enterprise also introduces the BigQuery reader, which, as the name implies, allows
the user to read data directly from BigQuery.
In ML tasks, speed is critical, and one of the major bottlenecks is the speed at which
data is accessed for the training process. TensorFlow Enterprise provides optimized
performance and easy access to data sources, making it extremely efficient on GCP.
Summary
In this chapter we explored different cloud service providers who could provide
the computing power necessary to train, evaluate, and deploy your deep learning
models. We started by first understanding the types of cloud computing services
available today. The chapter explored the Amazon, Google, and Microsoft IaaS
services for creating a virtual machine. The different infrastructure options
available in each were discussed. Next, we moved to SaaS services, specifically
Jupyter Notebook on cloud. The chapter covered the Amazon SageMaker, Google
Colaboratory, and Azure Notebooks. Just training a model is not sufficient;
eventually we want to deploy it in a scalable manner. Thus, we delved into
TensorFlow Extended, which allows users to develop and deploy ML models in
a scalable, safe, and secure manner. Lastly, we introduced TensorFlow Enterprise,
the latest offering in the TensorFlow ecosystem, and briefly discussed its features.
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