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学生对 deeplearning.ai 提供的 Deploying Machine Learning Models in Production 的评价和反馈

4.6
152 个评分
25 条评论

课程概述

In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving Introduction Week 2: Model Serving Patterns and Infrastructures Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging...

热门审阅

MN

Apr 21, 2022

This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!

WH

Sep 10, 2021

The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.

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1 - Deploying Machine Learning Models in Production 的 25 个评论(共 25 个)

创建者 Jordi W

Sep 30, 2021

So you have a fairly good understanding of ML modelling techniques, you played around with code in Jupyter notebooks and perhaps even got a TensorFlow docker image with GPU support to run on your local machine. You readily admit that there always is more to learn about modelling techniques, but you wonder how models run and are made available to users in a production environment? This course/specialization dives into just that question and a wide set of related subjects. A most important dimension of ML.

创建者 Roger S P M

Oct 2, 2021

Robert's lectures are terribly boring and there was no work to make his slides useful, they are just the words he is going to say.

创建者 Enrique C

Mar 14, 2022

Has some good and useful content but like the rest of the courses in this specialization it looks a lot like a Google cloud infomercial. The graded labs are ok. I have mixed feelings about ungraded ones as a few are really good and some others are a waste of time. I think that students need to be harder in the way they rate these types of courses to force the vendor to deliver quality labs end-to-end.

创建者 Stefan L

Feb 28, 2022

I​f you are doing the entire MLOps specialization, this coures won't bring much insight. If you don't you might learn something, i.e. regarding model serving. Unfortunately the labs are pure copy/paste exercise (qwiklabs) and do not yield any practical inisght. A missed opportunity.

创建者 Arthur F

Oct 2, 2021

pretty helpful broad overview of some of the tools and techniques used in deployment of ML models. Gives a good starting point for personal implementation since the field is clearly deep and fast evolving

创建者 Eoin B

Feb 6, 2022

Really enjoyed it however to get he most out of it, the time commitment is large

创建者 Gordon L W C

Oct 12, 2021

This course is what I think is missing in the market. A machine learning course with much emphasis on the practical aspects of running a machine learning platforms. I recommend it to anyone who is looking for the next step after you have finished training your model in Jupyter notebook. It is not the end but only the beginning.

创建者 Franco V

Oct 2, 2021

E​xcellent course and methodology. It helps me to improve my skills and expand my knowledge around the practice of MLOps. Exploring different tools and comparing them helps me to choose easily between them depending on each scenario.

创建者 Masoud A N

Apr 22, 2022

This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!

创建者 Walt H

Sep 11, 2021

T​he most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.

创建者 Travis H

Dec 19, 2021

Very insightful, with a good high-level explanation of challenges surrounding model usage and deployments in a production environment.

创建者 John L

Apr 17, 2022

This course has been so helpful and taught me so much information. A big thank you to all the instructors!!

创建者 Laxmikanta G

Dec 22, 2021

A wonderful course to get started with MLOps. I have really enjoyed reading through all of its contents

创建者 Vincent L

May 17, 2022

It's intense, applied, concrete and to the point. A very good course.

创建者 Kevin S

Feb 6, 2022

Broad overview of the many tools and techniques for real world ML ops

创建者 Fernandes M R

Sep 24, 2021

The first course of MLOps, and the best.

创建者 Thành H Đ T

Oct 6, 2021

I​ like this course. Thank you so much.

创建者 Alexandre B

Mar 5, 2022

Le meilleur cours de MLOps

创建者 Liang L

Oct 9, 2021

Relatable and hands-on.

创建者 Raspiani

Oct 2, 2021

Great, Thank's

创建者 EMO S L

Oct 18, 2021

Great course

创建者 Akie T

Apr 22, 2022

Good overview of major concept in the field, but expect to get just conceptual ideas and long to-do list of what you need to study somewhere else. Exercise (both graded and graded) are buggy and wasted a lot of time on non-essential details (like setting up the environment or just trying something in a different PC).

创建者 burhan r h

Jan 12, 2022

I was hoping for a final project that I can use in my portfolio because the course content is so much and not easy to digest

创建者 Prasanna M R

Oct 6, 2021

Awesome course with very good instructors . However in instructions in graded google cloud labs could be improved.

创建者 Afif A

Apr 20, 2022

it's a pretty good overview, only downside is the focus on GCP