Tutorial 4A: Scoring Pipeline Deployment Introduction

By the end of this tutorial, you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI.

Key:

Complete
Failed
Available
Locked
Objective
Select the "Read" button to begin.
Select the "Read" button to begin. Explore the goal of this tutorial
Prerequisites
Select the "Read" button to begin.
Select the "Read" button to begin. Find general information about what you need in order to complete this tutorial
Task 1: Tour of Prebuilt Experiment
Select the "Read" button to begin.
Select the "Read" button to begin. There are different Driverless AI experiments you could build with the Hydraulic System data and each label, which include cooler condition, valve condition, pump leakage, accumulator gas leakage, and stable flag [1]. For each experiment we could build, Driverless AI would choose a classification scorer because each of the labels have 2 or more categories.
Task 2: Scoring Pipeline Deployment Concepts
Select the "Read" button to begin.
Select the "Read" button to begin. In the scoring pipeline deployment concepts section, we cover the general machine learning workflow, model training, production deployment, edge inference, Driverless AI MOJO Scoring Pipelines and Python Scoring Pipelines, how to choose a scoring pipeline, productionizing scoring pipelines, challenges of productionizing scoring pipelines and making decisions based on a good model.
Task 3: Batch Scoring via Score Another Dataset
Select the "Read" button to begin.
Select the "Read" button to begin. You will learn to use Driverless AI’s Score Another Dataset button to perform batch scoring on data. Behind the scenes, Driverless AI uses the Python Scoring Pipeline to do batch scoring. You will download the batch scores csv file and import it into your favorite spreadsheet software, but in the tutorial, we import it to Google Sheets.
Task 4: Interactive Scoring (REST and AWS Lambda) via Deployment Templates
Select the "Read" button to begin.
Select the "Read" button to begin. You will learn to use Driverless AI’s Deploy (Local & Cloud) button to deploy the MOJO Scoring Pipeline to a REST Server and also an Amazon Lambda. Then once the MOJO is deployed into production, you as a client will use curl to send data and the prediction function you want the MOJO to execute to make real-time predictions on your data. When the MOJO finishes making the predictions, it will send the results back to you.
Task 5: Challenge
Select the "Read" button to begin.
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
Select the "Read" button to begin.
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, check out some H2O.ai webinars on this topic or check out what other companies or people are saying about using Driverless AI scoring pipelines.
Appendix A: Build Experiment
Select the "Read" button to begin.
Select the "Read" button to begin. You will import the Hydraulic System data using a Driverless AI Data Recipe, split the data into a training and test set, configure the Driverless AI experiment and then launch the experiment so that you can be ready to use either of the Driverless AI scoring pipelines to make predictions and more.
Appendix B: Glossary
Select the "Read" button to begin.
Select the "Read" button to begin. If there were 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.
You must be logged in to post to the discussion