Tutorial 4B: Scoring Pipeline Deployment Templates

This tutorial is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI's Production Ready Deployment Templates.

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Objective
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Select the "Read" button to begin. By the end of this tutorial, you will predict the cooling condition for a Hydraulic System by deploying a MOJO Scoring Pipeline into production using a Driverless AI Deployment Template.
Prerequisites
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Select the "Read" button to begin. For this tutorial, you will need a Driverless AI environment on Aquarium with the Prebuilt Experiment from Tutorial 4A: Scoring Pipeline Deployment Introduction, Driverless AI License, and full IAM access to AWS.
Task 1: Set Up Environment
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Select the "Read" button to begin. At this point, we have a prebuilt experiment with our Driverless AI Scoring Pipeline. Now, we are going to set up some dependencies for our environment, such as downloading the MOJO Scoring Pipeline, installing Java, Maven, Terraform, and specifying the Driverless AI License Key. Therefore, we will be ready to use the Driverless AI Deployment Template for executing our MOJO on a REST server or AWS Lambda.
Task 2: Scoring Pipeline Deployment Concepts
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Select the "Read" button to begin. In the concepts section, we cover the development components that are used for building Driverless AI Deployment Templates. The components you will learn about include the Gradle build system, Jenkins, Java Spring Boot Framework and Terraform. You will also learn about the REST server and AWS Lambda MOJO Scoring Pipeline deployment options.
Task 3: Interactive Scoring(REST and AWS Lambda) via Deployment Templates
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Select the "Read" button to begin. You will learn to use Driverless AI Deployment Templates (local-rest-scorer, AWS-lambda-scorer) to deploy the MOJO Scoring Pipeline to a REST server and also an Amazon Lambda. You will then act as a client who sends data along with the prediction function to the MOJO, so it will make real-time predictions and you will get back your scored results.
Task 4: Challenge
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Select the "Read" button to begin. You will be provided with some project ideas on how you can go further with your Scoring Pipeline Deployment knowledge. You can deploy a new scoring pipeline based on a new dataset, integrate a scoring pipeline directly into an existing program or deploy the scoring pipeline using a Driverless AI deployment template.
Next Steps
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Select the "Read" button to begin. After you complete the tutorial, we provide you with extra resources on what to do next. If you want to further your learning on scoring pipeline deployment you can go to our next tutorial, checkout some H2O.ai webinars on this topic or checkout what other companies or people are saying about using Driverless AI scoring pipelines.
Appendix A: Troubleshooting Deployment
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Select the "Read" button to begin. You may run into some issues when you try to deploy your MOJO scoring pipeline using one of the Driverless AI Deployment Templates. This section explores a few of the issues (such as “Error in function call” or “Provider produced inconsistent final plan”) you may run into when trying to deploy a scoring pipeline using the Amazon Lambda deployment template. So, we provide some potential solutions.
Appendix B: AI Glossary
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Select the "Read" button to begin. If there are terms in the tutorial that you want more clarification on like how it is relevant to H2O-3 or Driverless AI or a general definition, we provide you the link to the AI Glossary in H2O.ai Community.
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