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What you'll learn?
- 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
Course Overview
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 :-
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Introduction
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Flask Basics
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PROJECT : Deploying Breast Cancer Machine Learning Model using Flask
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Containerizing the Breast Cancer Model using Docker
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Streamlit Basics
-
PROJECT : Deploying Heart Disease Machine Learning Model using Streamlit
-
Containerizing the Heart Disease ML Model using Docker
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Serverless and AWS Basics
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AWS Lambda Basics
-
PROJECT : Deploying Diabetes Prediction Machine Learning Model using AWS Lambda
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General Overview of AWS Sagemaker
-
XgBoost using Sagemaker - Breast Cancer Classification
-
Hosting a Custom ML Model Using Sagemaker - Credit Card Fraud Detection
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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
Pre-requisites
- Basic of Machine Learning
Target Audience
- 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
Curriculum 109 Lectures 07:49:34
-
Section 1 : Introduction
-
Section 2 : Flask Basics
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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Section 10 : PROJECT : Deploying Diabetes Prediction Machine Learning Model using AWS Lambda
- 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
- 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
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Section 12 : Implementing XgBoost using Sagemaker - Breast Cancer Classification
- 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
- 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
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Section 14 : PROJECT : Deploying Diabetes Prediction Model using Sagemaker
- 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
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Section 15 : Conclusion
- Lecture 1 :
- Conclusion
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Instructor
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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!
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