Project: Custom Prediction Routine on Google AI Platform

von
Rhyme
In diesem angeleitetes Projekt werden Sie:

Deploy a model with custom prediction routine on Google AI Platform.

Use a model deployed on Google AI Platform for inference.

Clock2 hours
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

In this 2-hour long project-based course, you will learn how to deploy, and use a model on Google’s AI Platform. Normally, any model trained with the TensorFlow framework is quite easy to deploy, and you can simply upload a Saved Model on Google Storage, and create an AI Platform model with it. But, in practice, we may not always use TensorFlow. Fortunately, the AI Platform allows for custom prediction routines as well and that’s what we are going to focus on. Instead of converting a Keras model to a TensorFlow Saved Model, we will use the h5 ?le as is. Additionally, since we will be working with image data, we will use this opportunity to look at encoding and decoding of byte data into string for data transmission and then encoding of the received data in our custom prediction routine on the AI Platform before using it with our model. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python programming, Google Cloud Platform. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Kompetenzen, die Sie erwerben werden

Deep Learningcustom prediction routinegoogle ai platformMachine Learningkeras

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Introduction

  2. Notebook Instance and Model Artifact

  3. Testing the Model

  4. Custom Prediction Class

  5. Preprocessing

  6. Postprocessing

  7. Setup Script

  8. Deploying the Model

  9. Predictions

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein virtueller Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

Häufig gestellte Fragen

Häufig gestellte Fragen

  • By purchasing a guided project, you'll get everything you need to complete the guided project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

  • Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, guided projects are not available on your mobile device.

  • Guided project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

  • You can download and keep any of your created files from the guided project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

  • Financial aid is not available for guided projects.

  • Auditing is not available for guided projects.

  • At the top of the page, you can press on the experience level for this guided project to view any knowledge prerequisites. For every level of guided project, your instructor will walk you through step-by-step.

  • Yes, everything you need to complete your guided project will be available in a cloud desktop that is available in your browser.

  • You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.