Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.
Get started with your machine learning adventures by using state-of-the-art tools. We will talk about how to utilize the amazing work done by others to jump start your projects. We will also talk about making them more scalable and getting to a solution that can be used for production as well.
This talk provides an overview of TensorFlow Lite and its awesome tools and resources to help you create intelligent apps. I will walk through end-to-end computer vision examples with TFLite: from model training, conversion, optimization to model deployment on mobile and edge devices.
Coral is a complete toolkit to build products with local AI. Our on-device inferencing capabilities allow you to build products that are efficient, private, fast and offline. In this talk, we will introduce Coral and walk participants through current applications. In the second part, we will do some hands on demos to run inference using Coral devices.