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MLOps Certification- Basics, Deployment & Vertex AI Darknet

MLOps: Darknet, ML Flow, TFX & Helm for CI/CD deployment in ML systems and reliable monitoring of workflows in MLOps
4.0
4.0/5
(29 reviews)
9,090 students
Created by

8.9

CourseMarks Score®

10.0

Freshness

7.0

Feedback

9.2

Content

Platform: Udemy
Video: 2h 1m
Language: English
Next start: On Demand

Top MLOps courses:

Detailed Analysis

CourseMarks Score®

8.9 / 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 5/2022.

Course content can become outdated quite quickly.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

7.0 / 10
We analyzed factors such as the rating (4.0/5) 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

9.2 / 10
Video Score: 7.9 / 10
The course includes 2h 1m 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: 9.7 / 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:

13 articles.
28 resources.
0 exercise.
0 test.

Table of contents

Description

This course introduces participants to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems on both cloud and Edge. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course encompasses the following topics;
1. Introduction of Data, Machine Learning Model and Code with reference to MLOps.
2. MLOps vs DevOps.
3. Where and How to Deploy MLOps.
4. Components of MLOps.
5. Continuous X & Versioning in MLOps.
6. Experiment Tracking in MLOps.
7. Three Levels of MLOps.
8. How to Implement MLOps?
9. CRISP (Q)- ML Life Cycle Process.
10. Complete MLOps Toolbox.
11. ML Flow library for MLOps.
12. Tensor Flow Extended (TFX) for the deployment of MLOps.
13. PyCaret for the evaluation and deployment of MLOps.
14. Kubernetes as package manager for MLOps.
11. Google Cloud architectures for reliable and effective MLOps environments.
12. Working with AWS MLOps Services.

LAB Exercises with Solutions:
1. How to Deploy MLOps using Helm.
2. Make Changes with Helm.
3. Keep Track of Deployed Applications.
4. Share Helm Charts.

By the end of this course, you will be ready to:
•Design an ML production system end-to-end: data needs, modeling strategies, and deployment requirements.
•How to develop a prototype, deploy, and continuously improve a production-sized ML application.
•Understand data pipelines by gathering, cleaning, and validating datasets.
•Establish data lifecycle by leveraging data lineage.
•Use analytics to address model fairness and mitigate bottlenecks.
•Deliver deployment pipelines for model serving that require different infrastructures.
•Apply best practices and progressive delivery techniques to maintain a continuously operating production system.

You will learn

✓ MLOps- What are MLOps (Machine Learning Opeartions)?
✓ MLOps: Components including Continuous X & Versioning
✓ MLOps: Life Cycle Process ( End to End Learning Flow)
✓ MLOps: Model Testing & Model Packaging in PMML and ONNX
✓ MLOps: Workflow Decomposition & Production Environment
✓ MLOps: Pre- Computing Serving Patterns
✓ MLOps: Data, Machine Learning and Code Pipelines
✓ MLOps: Offline & Live Evaluation & Monitoring
✓ MLOPs: LinkedIn as a case example of large scale ML Deployment

Requirements

• No prior experience is needed. You will learn everything you need to know.

This course is for

• Beginner students and researchers curious to know about MLOps
• Individuals looking to enter the data and AI industry.

How much does the MLOps Certification- Basics, Deployment & Vertex AI Darknet course cost? Is it worth it?

The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.

Does the MLOps Certification- Basics, Deployment & Vertex AI Darknet course have a money back guarantee or refund policy?

YES, MLOps Certification- Basics, Deployment & Vertex AI Darknet 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 MLOps Certification- Basics, Deployment & Vertex AI Darknet course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Junaid Zafar a SCAM or a TRUSTED instructor?

Junaid Zafar has created 9 courses that got 405 reviews which are generally positive. Junaid Zafar has taught 42,687 students and received a 4.0 average review out of 405 reviews. Depending on the information available, Junaid Zafar is a TRUSTED instructor.
Dr. Engr. Junaid Zafar- Academician with 20 years experience
Prof. Dr. Engr. Junaid Zafar is currently working as Chairperson in Department of Electrical and Computer Engineering, Government College University, Lahore. He is also Director, Office of Research, innovation and Commercialization. He has completed his PhD in Electrical and Electronics Engineering, The University of Manchester University, UK, and BSc in Electrical Engineering from U.E.T Lahore. He is Academic visitor to the University of Cambridge, UK, MMU, UK and National University of Ireland. He remained Dual Degree programme coordinator at the Lancaster University, UK. Dr. Engr. Junaid Zafar received Roll of Honors for National Education Commission and Outstanding Teacher/ Researcher Awards from the Higher Education Commission, Pakistan. He is leading the macine learning and Artificial Intelligence centre with GC University, Lahore. He is member of Universal Association of Electronics & Computer Engineers, International Association of Computer Science & Information, and member of International Association of Engineers, IAENG Society of Artificial Intelligence, IAENG Society of Electrical Engineering, Science & Engineering Institute, IAENG Society of Imaging Engineering, Institute of Research Engineers & Doctors, and IAENG Society of Wireless Networks. He is member of editorial board in Journal of Future Technologies & Communications, Technical Programme committee, Frontiers of Information & Technologies, and Technical Programme Committee, Multi- Conference on Sciences & Technology. He is also serving as reviewer for IEEE Transactions on Microwave Theory & Techniques, IEEE Transactions on Antennas, IEEE Antenna & Wireless Propagation Letters, IEEE Transactions on Plasma Science, IEEE Transactions on Magnetics, International Journal of Electronics, and IET Antennas & Radio- wave Propagation. He has so far taught over twenty diffrent online courses based on outcome based student oriented models. He has also supervised more than 100 Masters/ MPhil thesis. He has published over 50 high impact factor publications and presented his work at several national and international renowned platforms.

8.9

CourseMarks Score®

10.0

Freshness

7.0

Feedback

9.2

Content

Platform: Udemy
Video: 2h 1m
Language: English
Next start: On Demand

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