Create & Deploy Data Science,Deep Learning Web Apps 2023

Learn development & deployment of machine learning and deep learning application projects with python on heruko

4.40 (13 reviews)
Udemy
platform
English
language
Data Science
category
instructor
346
students
3 hours
content
Nov 2023
last update
$49.99
regular price

What you will learn

Build Deep Learning Models

Deployment Of Deep Learning Applications

Deep Learning Practical Applications

How to use DEEP NEURAL NETWORKS for image classification

How to use ARTIFICIAL NEURAL NETWORKS

Description

Deployment of machine learning models means operationalizing your trained model to fulfill its intended business use case. If your model detects spam emails, operationalizing this model means integrating it into your company’s email workflow—seamlessly. So, the next time you receive spam emails, it’ll be automatically categorized as such. This step is also known as putting models into production.

Machine learning models are deployed when they have been successful in the development stage—where the accuracy is considered acceptable on a dataset not used for development (also known as validation data). Also, the known faults of the model should be clearly documented before deployment.

Even if your spam detection model has a 98% accuracy it doesn’t mean it’s perfect. There will always be some rough edges and that information needs to be clearly documented for future improvement. For example, emails with the words “save the date” in the subject line may always result in a spam prediction—even if it isn’t. While this is not ideal, deployment with some of these known faults is not necessarily a deal breaker as long as you’re able to improve its performance over time.

Models can integrate into applications in several ways. One way is to have the model run as a separate cloud service. Applications that need to use the model can access it over a network. Another way is to have the model tightly integrated into the application itself. In this case, it will share a lot of the same computing resources.

How the model integrates within your business systems requires careful planning. This should ideally happen before any development begins. The setup of the problem you are trying to solve and constraints under which models need to operate will dictate the best deployment strategy.

For example, in detecting fraudulent credit card transactions, we need immediate confirmation on the legitimacy of a transaction. You can’t have a model generate a prediction sometime today only to be available tomorrow. With such a time constraint, the model needs to be tightly integrated into the credit card processing application and be able to instantaneously deliver predictions. If over a network, it should incur very minimal network latency.

For some applications, time is not of the essence. So we can wait for a certain amount of data to “pile up” before the machine learning model is run on that data. This is referred to as batch processing. For example, the recommendations you see from a shopping outlet may stay the same for a day or two. This is because the recommendations are only periodically “refreshed.” Even if the machine learning models are sluggish, it doesn’t have a big impact as long the recommendations are refreshed within the expected time range.

Content

Pan Card Tempering Detector

Introduction To Pan Card Tempering Detector
Download the code
Loading libraries and dataset
Creating the pancard detector with opencv
Creating the Flask App
Creating Important functions
Deploy the app in Heruko
Deploy the app in Heruko 2
Testing the deployed pan card detector

Image Watermarking

Introduction
Download the code
Importing libraries and logo
Create text and image watermark
Creating the app
Deploying the app in heruko

Screenshots

Create & Deploy Data Science,Deep Learning Web Apps 2023 - Screenshot_01Create & Deploy Data Science,Deep Learning Web Apps 2023 - Screenshot_02Create & Deploy Data Science,Deep Learning Web Apps 2023 - Screenshot_03Create & Deploy Data Science,Deep Learning Web Apps 2023 - Screenshot_04

Reviews

Ancheng
February 26, 2022
The Python Flask code provided so far all has some level of issues that prevent them from being first-pass in the Heroku APP build and I have to spend extra hours debugging the 'requirements.txt' Python package versions to make them work. Not very good student experience there. # additional comment as the course continues almost all the web app deployment (Flask or Streamlit) is having this or that issues related to package incompatibility. It's almost certain that the requirements.txt file provided in the course download is not sufficient for reproducibility for the web app, also the instructor didn't provide the python version for the projects.
Ankita
March 7, 2021
I enjoyed this course as it was very practical. The deployment of DL projects was difficult, But good to get some exposure to this as well. I would highly recommend this course to people who are looking at learning ML, DL projects deployment into cloud and would appreciate a project based course.

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3885132
udemy ID
3/2/2021
course created date
3/7/2021
course indexed date
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