Overview of the Course:

  • Deploying Machine Learning and Deep Learning models using Streamlit

  • Deploying Machine Learning and Deep Learning models using Amazon Web Services (AWS)

  • Using Amazon SageMaker to build and deploy Machine Learning models

  • Deploy Deep Learning models using Flask API

Tools Covered

Projects covered in the Course:

  • Automating Loan Eligibility process

    Building an application that can automatically predict if the loan for a user will be approved or not.

  • Build your own Image Classification App

    In this project, we will use deep learning to solve a computer vision problem of identifying the object in an image. We will then deploy this project using streamlit on AWS.

  • Cardiac Arrest Prediction

    Predicting the chances of cardiac arrest based on the Physical and Demographic features of a person.

  • Typing Tutor

    In this project, we will build a dynamic code generator using deep learning. We will then host a website that would have a user interface experience to practice writing this generated code using streamlit on AWS.

  • Automatic Transcript Generation

    In this project, our aim is to parse audio from any given video for spoken English and convert this to written text using deep learning that can be used as a transcript. We will then wrap this code in an API and serve it using Flask.

Instructor(s)

  • Pulkit Sharma

    Pulkit Sharma

    Pulkit is a Data Scientist at Analytics Vidhya. His research area lies in the field of Computer Vision and Deep Learning. He has been working on various projects related to images and videos for the past few years. He is comfortable with Python, Keras, PyTorch and has done multiple projects using these frameworks and tools. Some of his key projects include Crowd Counting, Estimating the Screen Time in videos, Object Detection, and Image Segmentation. He is one of the primary content curators for Analytics Vidhya’s courses, such as the Computer Vision using Deep Learning and Applied Machine Learning. He is also an avid blogger and has written multiple detailed and in-depth guides on various computer vision topics and applications, ranging from Image Classification to Object Detection and Image Segmentation.
  • Faizan  Shaikh

    Faizan Shaikh

    Faizan is working as a data scientist at Analytics Vidhya. Being a Deep Learning enthusiast, he aims to utilize his skills to push the boundaries of AI research. Faizan is an avid blogger on Analytics Vidhya, and has contributed to many articles to explain complex concepts of Deep Learning in a simple manner. He will be your instructor for the course.

Course curriculum

  • 1
    Introduction to Model Deployment
    • Introduction to Model Deployment
    • Overview of the Course
    • Projects covered in the Course
    • Model Deployment tools covered in the Course
    • Instructors of the Course
    • Course Handouts
  • 2
    Pre-requisites
    • Advance Python, Basics of Machine Learning and Deep Learning
    • Introduction to git
    • Introduction to Linux Commands
    • Setting up your machine
  • 3
    Deploying Your First Machine Learning models
    • Outline of the Module
    • Quiz: Outline of the Module
    • Understanding the problem statement
    • Steps to build the Loan Eligibility Application
    • Quiz: Steps to build the Loan Eligibility Application
    • Frontend of the Loan Eligibility App
    • Quiz: Frontend of the Loan Eligibility application
    • Deploying rule based model using streamlit
    • Exercise: Deploying rule based model using Streamlit
    • Deploying machine learning model using streamlit
    • Exercise: Deploying machine learning model using Streamlit
    • Summarizing the module
  • 4
    Assignment: Building a Big Mart Sales Prediction Application
    • Build a Big Mart Sales Prediction Application
  • 5
    Deploying Machine Learning model using Streamlit + AWS
    • Introduction to Amazon Web Services (AWS)
    • Quiz: Introduction to Amazon Web Services
    • Spinning up an AWS server
    • Quiz: Spinning up an AWS server
    • Deploying MLmodel using Streamlit and AWS
    • Quiz: Deploying ML model using Streamlit and AWS:-
  • 6
    Deploying Image Classification model using Streamlit + AWS
    • Overview of the module
    • Building an Image Classification model
    • Exercise: Building an Image Classification model
    • Deploying Image Classification model using Streamlit and AWS (Part I)
    • Deploying Image Classification model using Streamlit and AWS (Part II)
    • Quiz: Deploying Image Classification model using Streamlit and AWS
  • 7
    Deploying Text Generation model using Streamlit + AWS
    • Understanding Text Generation Project
    • Create front-end of the Project
    • Create front-end of the Project - Implementation
    • Quiz: Create front-end of the Project
    • Building a Text Generation model
    • Exercise: Building a Text Generation model
    • Deploying Text Generation model using Streamlit
    • Quiz: Deploying Text Generation model using Streamlit
    • Deploying Text Generation model using Streamlit on AWS
    • Setting up an accessible website
  • 8
    Assignment: Deploying Image-based Gender Classification model using Streamlit + AWS
    • Assignment: Deploying Image-based Gender Classification model using Streamlit + AWS
  • 9
    Deploying models as Web Applications
    • Outline of the module
    • Introduction to Amazon Sagemaker
    • Quiz: Introduction to Amazon SageMaker
    • Understanding the problem statement: Cardiac Arrest Predictor
    • Quiz: Understanding the Problem Statement: Cardiac Arrest Predictor
    • Setting up Amazon SageMaker
    • Building a Machine Learning model on Amazon Sagemaker
    • Exercise: Building a machine learning model on sagemaker
    • Deploying the Machine Learning model using Amazon Sagemaker
    • Using SageMaker Endpoint to generate Inferences
  • 10
    Assignment: Build and Deploy Big Mart Sales Prediction model on Amazon SageMaker
    • Build and Deploy Big Mart Sales Prediction model on Amazon SageMaker
  • 11
    Deploying ML model as APIs using Flask
    • Introduction to Flask for Model Deployment
    • Deep dive into APIs
    • Quiz: Deep dive into APIs
    • Understanding the Problem Statement
    • Building an ML model for Cardiac Arrest Prediction
    • Exercise: Building an ML model for Cardiac Arrest Prediction
    • Deploying ML model using Flask
  • 12
    Deploying DL model as APIs using Flask
    • Understand the Project - Transcript Generation
    • Building a DL model for Transcript Generation
    • Deploying Transcript Generation model using Flask
    • Exercise: Deploying Transcript Generation model using Flask
  • 13
    Assignment: Deploying Urban Sound Classification using Flask
    • Assignment: Deploying Urban Sound Classification using Flask
  • 14
    Conclusion
    • Summary and Where to go from here