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Create And Deploy Deep Learning Project Web Apps

Learn deployment of machine learning and deep learning projects with python on heruko
4.6
4.6/5
(5 reviews)
262 students
Created by Pianalytix .

9.3

CourseMarks Score®

10.0

Freshness

8.3

Feedback

9.0

Content

Platform: Udemy
Price: $11.99
Video: 2h 46m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 3/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.3 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.0 / 10
Video Score: 8.0 / 10
The course includes 2h 46m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

5 articles.
10 resources.
0 exercise.
0 test.

Table of contents

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.

Requirements

• Knowledge Of Deep Learning
• Knowledge Of Machine Learning

You will learn

✓ Build Deep Learning Models
✓ Deployment Of Deep Learning Applications

This course is for

• Beginners In Machine Learning

How much does the Create And Deploy Deep Learning Project Web Apps course cost? Is it worth it?

The course costs $11.99. And currently there is a 82% discount on the original price of the course, which was $64.99. So you save $53 if you enroll the course now.

Does the Create And Deploy Deep Learning Project Web Apps course have a money back guarantee or refund policy?

YES, Create And Deploy Deep Learning Project Web Apps has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Create And Deploy Deep Learning Project Web Apps course, but there is a $53 discount from the original price ($64.99). So the current price is just $11.99.

Who is the instructor? Is Pianalytix . a SCAM or a TRUSTED instructor?

Pianalytix . has created 14 courses that got 494 reviews which are generally positive. Pianalytix . has taught 14,049 students and received a 4.2 average review out of 494 reviews. Depending on the information available, Pianalytix . is a TRUSTED instructor.

More info about the instructor, Pianalytix .

Technology For Innovators
Pianalytix Edutech Pvt Ltd uses cutting-edge AI technology & innovative product design to help users learn Machine Learning more efficiently and to implement Machine Learning in the real world. Pianalytix also leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency and increasing profitability.

9.3

CourseMarks Score®

10.0

Freshness

8.3

Feedback

9.0

Content

Platform: Udemy
Price: $11.99
Video: 2h 46m
Language: English
Next start: On Demand

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