If your model detects spam emails, operationalizing this model means integrating it into your company's email workflow—seamlessly. So, the next 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 .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 bs. 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 limacy of a transaction. You can't have a model generate a prediction some today only to be available tomorrow. With such a 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, 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 range.

Overview
Section 1: Introduction

Lecture 1 Introduction To The Course

Lecture 2 Udemy Course Feedback

Section 2: Pan Card Tempering Detector

Lecture 3 Introduction To Pan Card Tempering Detector

Lecture 4 the code

Lecture 5 Loading libraries and dataset

Lecture 6 Creating the pancard detector with opencv

Lecture 7 Creating the Flask App

Lecture 8 Creating Important functions

Lecture 9 Deploy the app in Heruko

Lecture 10 Deploy the app in Heruko 2

Lecture 11 Testing the deployed pan card detector

Section 3: Project On Image Watermarking

Lecture 12 Introduction

Lecture 13 the code

Lecture 14 Importing libraries and logo

Lecture 15 Create text and image watermark

Lecture 16 Creating the app

Lecture 17 Deploying the app in heruko

Section 4: Project On Text Extraction From Images

Lecture 18 Introduction

Lecture 19 Importing libraries and data

Lecture 20 Extracting the test from image

Lecture 21 Modifiying the extractor

Lecture 22 creating the extractor app

Lecture 23 running the extractor app

Lecture 24 the code

Section 5: Project On Plant Disease Prediction

Lecture 25 Introduction

Lecture 26 Importing libraries and data

Lecture 27 Understanding the data and data preprocessing

Lecture 28 Model building

Lecture 29 Creating an app using streamlit

Lecture 30 the code

Section 6: Project On Counting & Detecting Vehicles

Lecture 31 Introduction

Lecture 32 Importing libraries and data

Lecture 33 Transfog Images and creating output

Lecture 34 Creating a flask APP

Lecture 35 the code

Bners In Machine Learning

HomePage:

Https://anonymz.com/https://www.udemy.com/course/deployment-of-deep-learning-web-applications-projects/

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