IBM Watson AutoAI machine learning tutorial | Connecting model API to a web application

Channel:
United States IBM Developer
Subscribers:
73,500
Published on ● Video Link: https://www.youtube.com/watch?v=su79BFQ9VU0


Category:
Tutorial
Duration: 6:15
2,424 views
41

This is the third video in a three-part series from @Horea Porutiu.

Part 1: https://youtu.be/knxbJgPmD5E
Part 2: https://youtu.be/E_ArfAy3A4k
Watson Machine Learning web-app demo using AutoAI: https://youtu.be/B1WAOiO1o4o

In this video, we deploy our best performing model as a web-service so that we can connect to our running machine learning model via API. Next, we test our model to ensure it is working properly. Lastly, we implement the API that will call our deployed model. We need the IBM Watson Machine Learning API key, the instance ID, and the model deploymentID. Lastly, we need a token to authenticate ourselves. Once we paste all of the credentials into our Python, Flask application, we can install our dependencies with Pip and then run the Flask application. Lastly, we will test our application by using the web-app UI to fill in a form to talk to our running machine learning model. And that's it! Within 20 minutes, you've been able to explore a free data set on Kaggle, upload it to IBM Watson, and automatically generate several pipelines that used various machine learning algorithms to predict insurance expenses. We then deployed our model and connected to it via API in a Python application. Great job! For code, and detailed instructions, check the GitHub link below.

- Free IBM Cloud account needed, register here: https://tinyurl.com/y4mzxow5

- The data set: https://www.kaggle.com/noordeen/insurance-premium-prediction

- Project GitHub: https://github.com/IBM/predict-insurance-charges-with-ai

Build Smart. Build secure. Join a global community of developers at http://ibm.biz/IBMdeveloperYT

#MachineLearning
#WebApplication
#AutoAI



Other Videos By IBM Developer


2020-06-18Jaya Ramanathan | Governance & Security | Origin Story
2020-06-18Rosalind Radcliffe | DevSecOps | Origin Story
2020-06-18Dan Walsh | Containers & Security | Origin Story
2020-06-18Nigel Brown | Developer Advocate | Origin Story
2020-06-17NuArca and IBM Cloud help Fin Tech Social Distance, during COVID-19
2020-06-12Cloud-native CI/CD with Tekton - Part Three
2020-06-12Tekton Pipelines | This tutorial dives into using Tekton Pipelines. Part Two
2020-06-09Making AI Trustworthy and Transparent with CODAIT and Partnership on AI
2020-06-04Build a machine learning node for Node-RED using TensorFlow.js
2020-05-28Real-Time Object Detection in the Browser with TensorFlow.js
2020-05-27IBM Watson AutoAI machine learning tutorial | Connecting model API to a web application
2020-05-27IBM Watson AutoAI machine learning tutorial | Running AutoAI
2020-05-27IBM Watson AutoAI machine learning tutorial | Data exploration and visualization
2020-05-27Pitbull Supports Call for Code
2020-05-22Tekton Basics | An overview of Tekton for creating CI/CD systems
2020-05-22Intro to cloud native: What are containers?
2020-05-19What is IBM Cloud Pak for Data? — IBM Developer
2020-05-19Data Virtualization in Action - IBM Cloud Pak for Data
2020-05-13Event-driven reactive programming in Java
2020-05-12HPC Lung Simulation aids in COVID-19 fight, Dr. Arabshahi and Dr. Skjellum from SimCenter @ UTC.edu
2020-05-12HPC Simulations of Biological Systems to Understand and Treat Cancer with Dr. Carlos F. Lopez


Tags:
Software Development
Application Development
Artificial Intelligence
Machine Learning
Cloud Computing
Data Analytics
Technology
programming
coding
programming languages
learn coding
learn to code
mobile application
IBM
IBM Developer
IBM Code
open source
open technology
autoAI
Web application
API