Deploying Machine Learning Models - A Complete Guide

Learn to Deploy Machine Learning Models. Learn about Server and Serverless Frameworks Both using Python

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Last Updated: 2023-03-22 11:03:50 English Course Type: Self Learning

Learn the Basics of Flask
Learn to Deploy Machine Learning Model using Flask
Learn the Basics of Steamlit
Learn to Deploy Machine Learning Model using Streamlit
Learn Serverless and AWS Lambda
Learn to Deploy Machine Learning Model using Serverless and Lambda
Learn Basics of Docker
Learn to Deploy Machine Learning Model using Docker Container
Learn To Deploy model on AWS Sagemaker

Deploying Machine Learning Models - A Complete Guide

Learn to Deploy Machine Learning Models. Learn about Server and Server less Frameworks Both using Python

So , YOU HAVE A MACHINE LEARNING MODEL and IT IS WORKING Well ! NOW, WHAT ?

This course will help you in Deploying your Machine Learning Models in all Possible Ways Possible. We will be use Server Based and Server Less Frameworks both.

In This Course, You will Learn :-

Introduction
Flask Basics
PROJECT : Deploying Breast Cancer Machine Learning Model using Flask
Containerizing the Breast Cancer Model using Docker
Streamlit Basics
PROJECT : Deploying Heart Disease Machine Learning Model using Streamlit
Containerizing the Heart Disease ML Model using Docker
Serverless and AWS Basics
AWS Lambda Basics
PROJECT : Deploying Diabetes Prediction Machine Learning Model using AWS Lambda
General Overview of AWS Sagemaker
XgBoost using Sagemaker - Breast Cancer Classification
Hosting a Custom ML Model Using Sagemaker - Credit Card Fraud Detection
PROJECT : Deploying Model using Sagemaker, AWS Lambda and API Gateway - Diabetes Prediction

We know that you're here because you value your time and Money.By getting this course, you can be assured that the course will explain everything in detail and if there are any doubts in the course, we will answer your doubts in less than 12 hours.

All the project Files are available for you.

So, What are you waiting for? Go Click on the Buy button and let's explore this exciting journey of Deploying Machine Learning Models

I will be waiting for you inside the course...

Cosmic

Basic of Machine Learning

Anyone Willing to learn how to Deploy machine Learning Model
Beginner Machine Learning or Data Science Professional Willing to Enhance their Skills
Intermediate Machine Learning or Data Science Professional Willing to Enhance their Skills
Advance Machine Learning or Data Science Professional Willing to Enhance their Skills

Section 1 : Introduction 1 Lectures 00:03:28
Lecture 1 :
Introduction Preview
Section 2 : Flask Basics 4 Lectures 00:15:06
Lecture 1 :
Intro to Flask and Making a basic API
Lecture 2 :
Recieving Data through GET Request
Lecture 3 :
Recieving Data through POST Request
Lecture 4 :
Making a HTML Form to Send inputs and Receive the Output from API
Section 3 : PROJECT : Deploying Breast Cancer Machine Learning Model using Flask 10 Lectures 01:01:19
Lecture 1 :
General Overview and Business Challenge
Lecture 2 :
Importing Data and basic Cleaning
Lecture 3 :
Data Visulization, Feature Scaling and Encoding
Lecture 4 :
Model Fitting and Getting the Feature importance
Lecture 5 :
Balancing the dataset and Feature Selection
Lecture 6 :
Deploying The Model ( Part 1 )
Lecture 7 :
Deploying The Model ( Part 2 )
Lecture 8 :
Deploying The Model ( Part 3 )
Lecture 9 :
Deploying The Model ( Part 4 )
Lecture 10 :
Conclusion
Section 4 : Containerizing the Breast Cancer Model using Docker 4 Lectures 00:15:46
Lecture 1 :
General Overview of Docker
Lecture 2 :
Coding the Dockerfile
Lecture 3 :
Minor PreRequisite for Containerizing
Lecture 4 :
Building and Running the Container
Section 5 : Streamlit Basics 8 Lectures 00:26:31
Lecture 1 :
Intro and installing Streamlit
Lecture 2 :
Showing Dataframe and Plotting LineChart
Lecture 3 :
Showing Maps and Toggling CheckBoxes
Lecture 4 :
Conditional Rendering using Select and MultiSelect Box
Lecture 5 :
Example : Predict House Price : Getting Basics Ready
Lecture 6 :
Example : Predict House Price : Making the UI
Lecture 7 :
Example : Predict House Price : Training the Model and Showing Prediction on UI
Lecture 8 :
Project File
Section 6 : PROJECT : Deploying Heart Disease Machine Learning Model using Streamlit 11 Lectures 00:47:00
Lecture 1 :
General Overview and Business Challenge
Lecture 2 :
Data import and Basic Data Cleaning
Lecture 3 :
Visualization and EDA
Lecture 4 :
Feature Engineering
Lecture 5 :
Model Building and Evaluation
Lecture 6 :
Minor Bug Fixes
Lecture 7 :
Balancing the Dataset
Lecture 8 :
Deploying the Model ( Part 1 )
Lecture 9 :
Deploying the Model ( Part 2 )
Lecture 10 :
Deploying the Model ( Part 3 )
Lecture 11 :
Conclusion
Section 7 : Containerizing the Heart Disease ML Model using Docker 4 Lectures 00:13:24
Lecture 1 :
General Overview of Docker
Lecture 2 :
Coding the Dockerfile
Lecture 3 :
Requirement.txt File and Building the Docker Image
Lecture 4 :
Running the Container
Section 8 : Serverless and AWS Basics 4 Lectures 00:10:47
Lecture 1 :
What is Serverless Computing?
Lecture 2 :
Function as a Service ( FaaS)
Lecture 3 :
Advantage of Serverless Computing
Lecture 4 :
Disadvantages of Serverless Computing
Section 9 : AWS Lambda Basics 4 Lectures 00:24:03
Lecture 1 :
Intro
Lecture 2 :
Getting Started with AWS and Serverless and Adding Configs
Lecture 3 :
Creating Serverless and AWS Project using Terminal
Lecture 4 :
Deploy the Project on AWS
Section 10 : PROJECT : Deploying Diabetes Prediction Machine Learning Model using AWS Lambda 14 Lectures 00:46:21
Lecture 1 :
General Overview and Business Challenge
Lecture 2 :
Importing Data and basic Cleaning
Lecture 3 :
Visualization and EDA
Lecture 4 :
Feature Engineering
Lecture 5 :
Model Building
Lecture 6 :
Balancing the dataset
Lecture 7 :
Refitting the Model
Lecture 8 :
Deploying the Model ( Part 1 )
Lecture 9 :
Deploying the Model ( Part 2)
Lecture 10 :
Deploying the Model ( Part 3)
Lecture 11 :
Deploying the Model ( Part 4 )
Lecture 12 :
Deploying the Model ( Part 5)
Lecture 13 :
Deploying the Model ( Part 6)
Lecture 14 :
Conclusion
Section 11 : General Overview of SageMaker 9 Lectures 00:26:41
Lecture 1 :
Intro to AWS Sagemaker
Lecture 2 :
Instance Type
Lecture 3 :
Built in Algorithm
Lecture 4 :
Frameworks Offered , AWS Ground Truth and NEO
Lecture 5 :
Different API Levels
Lecture 6 :
Making a S3 Bucket
Lecture 7 :
Spinning Jupyter Notebook in Sagemaker ( Part 1)
Lecture 8 :
Spinning Jupyter Notebook in Sagemaker ( Part 2)
Lecture 9 :
Summary
Section 12 : Implementing XgBoost using Sagemaker - Breast Cancer Classification 12 Lectures 01:03:32
Lecture 1 :
General Overview and Business Problem
Lecture 2 :
Data importing and Basic Data Cleaning
Lecture 3 :
Visualization, Scaling and Encoding Data
Lecture 4 :
Model Fitting and getting the Feature Importance
Lecture 5 :
Setting up everything for AWS Sagemaker
Lecture 6 :
Saving Data in S3
Lecture 7 :
Model Building and Fitting
Lecture 8 :
End Point Creation
Lecture 9 :
Prediction ( Part 1 )
Lecture 10 :
Prediction ( Part 2 )
Lecture 11 :
Conclusion
Lecture 12 :
Balancing dataset and Feature Selection
Section 13 : Hosting Custom Model on Sagemaker - Credit Fraud Detection 10 Lectures 01:09:19
Lecture 1 :
Intro and Business Challenge
Lecture 2 :
Data Import
Lecture 3 :
Feature Engineering and Model Prediction
Lecture 4 :
Under Sampling the Data
Lecture 5 :
Over Sampling the Data
Lecture 6 :
Understanding the Folder Structure AWS Accepts
Lecture 7 :
Basic Setup and Model Python File
Lecture 8 :
Basic Imports and Upload to S3
Lecture 9 :
Model Building, Fitting and End Point Creation
Lecture 10 :
Conclusion
Section 14 : PROJECT : Deploying Diabetes Prediction Model using Sagemaker 13 Lectures 00:45:26
Lecture 1 :
General Overview and Business Challenge
Lecture 2 :
Importing Data
Lecture 3 :
Visualization and EDA
Lecture 4 :
Feature Engineering
Lecture 5 :
Model Building
Lecture 6 :
Balancing the dataset
Lecture 7 :
Refitting the Model
Lecture 8 :
Switching everything to AWS Notebook Instance
Lecture 9 :
Model Building and Fitting
Lecture 10 :
End Point Creation
Lecture 11 :
Making the SLS Project and Coding the YAML File
Lecture 12 :
Coding the Handler file, Deployment and Testing
Lecture 13 :
Conclusion
Section 15 : Conclusion 1 Lectures 00:00:51
Lecture 1 :
Conclusion

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Cosmic .,

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Hi, We are Cosmic . Professionally, We are Entrepreneurs with over six years of experience in finance, Tech, Real Estate etc. We are trained at Mobile App Development, Web Development, Artificial Intelligence, Finance etc. In my courses, you will very well understand how I combine theory and Real Life Case studies to give you the best in-Class Training in any topic IÂ teach. I am looking forward to sharing my passion and knowledge with you! Looking forward to working together!