Creating a Scalable Machine Learning Pipeline

Creating a Scalable Machine Learning Pipeline, available at $54.99, has an average rating of 4.3, with 49 lectures, based on 33 reviews, and has 1084 subscribers.

You will learn about The course will focus on what to build once you have a Machine Learning Model. Allowing you to improve and monitor your deep learning model in production. Tensorflow Js, Firebase, Material UI, React This course is ideal for individuals who are Software Developers, Data Scientists, Machine Learners, Entrepreneurs It is particularly useful for Software Developers, Data Scientists, Machine Learners, Entrepreneurs.

Enroll now: Creating a Scalable Machine Learning Pipeline

Summary

Title: Creating a Scalable Machine Learning Pipeline

Price: $54.99

Average Rating: 4.3

Number of Lectures: 49

Number of Published Lectures: 48

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 48

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

The course will focus on what to build once you have a Machine Learning Model. Allowing you to improve and monitor your deep learning model in production.

Tensorflow Js, Firebase, Material UI, React

Who Should Attend

Software Developers, Data Scientists, Machine Learners, Entrepreneurs

Target Audiences

Software Developers, Data Scientists, Machine Learners, Entrepreneurs

I show you you everything you need to start using your tflite and tensorflow.js machine learning models in production. Create a website that allows users to upload images, get predictions from your custom machine learning model and review the performance of the model in real time.

Whether you already have a computer vision model or not I show you how to easily create one and ultimately use and deploy it to production. Learn how to use your own custom models with tensorflow.js,  allowing users to upload images and get predictions back on that image.

We create an entire pipeline that allows you to improve and monitor your machine learning model’s over time. Allow users to upload new images for predictions, saving those predictions and then using the new images as training data to improve our custom models performance.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Project Demo

Chapter 2: Project Overview

Lecture 1: Technologies Used

Chapter 3: Data Gathering and Collection

Lecture 1: Easy Image Gathering

Chapter 4: Google AutoML Vision: Image Classification

Lecture 1: Demo

Lecture 2: About AutoML

Lecture 3: Importing Images to AutoML Vision

Lecture 4: Labeling Images

Lecture 5: Training our Model

Lecture 6: Training a Model for Cloud Use

Lecture 7: evaluate model

Lecture 8: Export Model

Chapter 5: Website Setup and Creation

Lecture 1: Setting up our Website

Lecture 2: Use Model Export

Lecture 3: Adding React Router – Navigation Logic

Lecture 4: Adding and Connecting Firebase to our Website

Chapter 6: Using Image Classification Models

Lecture 1: Image Uploader

Lecture 2: Get Images for Inference

Lecture 3: Make Predictions

Lecture 4: Fix CORS Storage

Lecture 5: Save Predictions

Lecture 6: Adding and Styling our Navigation Bar

Lecture 7: Auto Starting our Classification Inference Process

Chapter 7: Demo to Send to Friends

Lecture 1: Simple Image Classification Demo

Lecture 2: Creating a User Demo Page with File Upload

Chapter 8: Triggering the Piepline

Lecture 1: Cloud Function Setup / Overview

Lecture 2: Checking User Submitted Images

Lecture 3: Checking User Submitted Images Continued

Lecture 4: Tracking Images Uploaded by Users

Lecture 5: Create a CSV with Image File Names and Locations

Lecture 6: Update the Models Image Count

Lecture 7: Update the Models Stats and Analytics

Lecture 8: Updating the Models Labels Stats and Analytics

Chapter 9: Machine Learning Dashboard

Lecture 1: Creating our Machine Learning Dashboard

Lecture 2: Connect Dashboard to our Models Analytics

Lecture 3: Connect Dashboard to our Labels Analytics

Lecture 4: Exporting and Saving our Data

Lecture 5: Cron Job – Schedule Model Stat Export

Lecture 6: Cron Job -Schedule Label Stat Export

Chapter 10: Human In The Loop

Lecture 1: Intro to Human In the Loop: RevaliML

Lecture 2: Create API to Review Predictions

Lecture 3: Create API to Update and Save Reviewed Predictions

Chapter 11: Deployment

Lecture 1: Deploy Classification Front-End

Lecture 2: Deploying Our Dashboard

Chapter 12: Other: Cleaning up and Finishing Project

Lecture 1: Displaying Historical Model Data

Lecture 2: Displaying Model Data in Charts

Lecture 3: Connect the Label Volume to Dashboard Donut Chart

Lecture 4: Functions Cleanup: Add to Images

Instructors

Charles Svetich
Maker and Builder of Scaleable Things

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 8 votes

4 stars: 12 votes

5 stars: 12 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!