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Отзывы учащихся о курсе Deploying Machine Learning Models in Production от партнера deeplearning.ai

4.7
звезд
Оценки: 73
Рецензии: 12

О курсе

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...

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1–12 из 12 отзывов о курсе Deploying Machine Learning Models in Production

автор: Jordi W

30 сент. 2021 г.

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.

автор: Roger S P M

2 окт. 2021 г.

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.

автор: Arthur F

2 окт. 2021 г.

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

автор: Gordon L W C

12 окт. 2021 г.

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.

автор: Franco V

2 окт. 2021 г.

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.

автор: Walt H

11 сент. 2021 г.

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

автор: Fernandes M R

24 сент. 2021 г.

The first course of MLOps, and the best.

автор: Thành H Đ T

6 окт. 2021 г.

I​ like this course. Thank you so much.

автор: Liang L

9 окт. 2021 г.

Relatable and hands-on.

автор: Raspiani

2 окт. 2021 г.

Great, Thank's

автор: EMO S L

18 окт. 2021 г.

Great course

автор: Prasanna M R

6 окт. 2021 г.

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