Chevron Left
Вернуться к Machine Learning Modeling Pipelines in Production

Отзывы учащихся о курсе Machine Learning Modeling Pipelines in Production от партнера deeplearning.ai

4.5
звезд
Оценки: 126
Рецензии: 23

О курсе

In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. 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: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability...

Лучшие рецензии

JS
13 сент. 2021 г.

Excellent content and lectures from Mr. Robert . Thank you very much Sir for the excellent way of explaining these difficult topics . Thank you !!!

MB
20 окт. 2021 г.

I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.

Фильтр по:

1–25 из 27 отзывов о курсе Machine Learning Modeling Pipelines in Production

автор: Folkert S

18 сент. 2021 г.

I thought this course was ok. On the one hand, the theory that is taught is quite general and trivial, while on the other hand, the technical focus is mostly on Google's tools and deep learning. As getting machine learning to production is an advanced task and requires a broad set of skills, I would've, for instance, expected this course to be more on structured data. Also, most of the labs, especially the GCP ones, feel just like copying and pasting some commands, it's not that challenging and therefore I didn't learn a lot there.

автор: Cosmin K

16 авг. 2021 г.

Great material, insigthful notebooks and a valuable review of numerous concepts and tools! The course set me on track with steps to take and pitfalls to evoid. Thank you! Now is practice and continous learning from my part.

автор: Thành H Đ T

24 авг. 2021 г.

wow, Its very good

автор: Peter W

9 авг. 2021 г.

Covers a lot of content at a high level. One slight criticism is that the graded exercises focused on Google cloud and didnt require much thought. The ungraded labs on the other hand were quite interesting.

автор: Hieu D T

15 авг. 2021 г.

A bit dependent on GCP, took me quite a decent amount of time to do network setting. You should use your own internet, do not use one behind corporate proxy like I did. Materials and guides are great.

автор: Ashwani K

7 авг. 2021 г.

Some of the topics were too advanced and instructor assumes that we know those basics. It felt rush through little bit and more of reading slides then explaining at many places

автор: Andrei

9 сент. 2021 г.

need to improve the explanation of topics

автор: Yixin D

13 окт. 2021 г.

I find this course extremely hard to follow, some main and tricky concepts are only covered by a mere sentence in the lecture.

автор: Roger S P M

5 сент. 2021 г.

So Boring!

автор: Hitesh K

18 июля 2021 г.

So far the most informative course in this specialization. This course has actually taught me how different is ML in production than doing simple Ml stuff on notebook for academic or research purpose. You get to see the bigger picture, i.e, different and bigger constraints that needs to be addressed for deploying any model to be on systems, specially edge devices.

автор: Jonathan S R P

28 сент. 2021 г.

I strongly recommend this course to anyone interested in MlOps and how to manage a ML pipeline in production, i learn a lot about pipelines, distillation and interpretable models. Can wait to put all this knowledge in practice :)

автор: Umberto S

29 авг. 2021 г.

Great course! One of the most clear and extended courses by DeepLearning.ai. I think It covers in an excellent way all topics to understand what MLOps is and how to approach it in the right way.

автор: Jitendra S

14 сент. 2021 г.

Excellent content and lectures from Mr. Robert . Thank you very much Sir for the excellent way of explaining these difficult topics . Thank you !!!

автор: Melanie J B

21 окт. 2021 г.

I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.

автор: Nhan N L

21 сент. 2021 г.

This course is helpful. Enrich my knowledge with data concepts, optimal high-performance model tools and model debugging.

автор: Mario T

5 сент. 2021 г.

Outstanding! Exceptionally informative. Makes me look way aheady how to implement ML pipelines, and how to analyze them.

автор: vadim m

4 авг. 2021 г.

Covers a lot of hot topics related to ML Modeling pipelines in production with great breadth and depth.

автор: Reza M

14 сент. 2021 г.

T​his is very helpful course to understand the life of model specially after its deployment.

автор: Cees R

15 окт. 2021 г.

This course filled in some black holes in my knowledge and I found it very helpful.

автор: amadou d

8 авг. 2021 г.

Excellent!! Ver, Very Very Good. Learn a lot. Thank you for sharing.

автор: Kiran K

22 июля 2021 г.

Good But More practical needed with theory

автор: Fernandes M R

24 сент. 2021 г.

The first course of MLOps, and the best.

автор: Илья В

9 сент. 2021 г.

great course, a lot of stuff

автор: Liang L

22 июля 2021 г.

Good content and hands on.

автор: Raspiani

28 авг. 2021 г.

Awesome Thanks