Tag: Model Deployment

Course content

• Welcome to the Course!
• Day 1: Develop an A1 model to classify fashion elements using Google Teachable
• Day 2: Deep Dive into A1 technicalities
• Day 3: Detect and classify face masks using Google Teachable Machines
• Day 4: Visualize Artificial Intelligence Models Using Tensorspace.JS and GTP
• Day 5: Develop an ML Model to predict used car prices using DataRobot
• Day 6: Develop an A1 model to predict employee’s attrition using DataRobot
• Day 7: Develop an A1 model to detect Diabetic Retinopathy Using DataRobot
• Day 8: Deploy an A1 model to predict customer sentiment from Text
• Day 9: Predict credit card default using AWS SageMaker Autopilot
• Day 10: Google Vertex A1-Powered Regression Model Prediction
• Congratulations!! Don’t forget your Prize 🙂

Course content

• Introduction
• Why learn TenserFlow
• Setting up the TensorFlow Environment
• A1 and Machine Learning Concepts
• Applying the Machine Learning Workflow with TensorFlow
• Understanding Neural Networks
• Building and Training Your First Neural Network
• Monitoring and Improving Neural Network Performance
• Deploying Your Neural Network
• Assignment
• Conclusion and Final Words

Day 1:

• Introduction

Module 1: KNIME Server:

Collaboration – Connecting and Deploying Items to KNIME Server from KNIME Analytics Platform

• How to Connect to KNIME Server
• Permission Settings on KNIME Server

Module 2: KNIME Server: Automation & Deployment

Automation and Deployment – Remote Execution and KNIME WebPortal

• Remote Execution on KNIME Server
• KNIME Remote Workflow Editor
• KNIME WebPortal

Day 2:

Module 3: KNIME Server: Management

Management – Versioning and Workflow Difference

• Versioning
• Workflow Comparison
• Node Comparison

Module 4: Overview of KNIME Analytics Platform

• Controlling the model flow
• Model deployment on KNIME Server
• Test Scenarios between KNIME AP & Server
• Summary and Conclusion