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

Оценки: 165
Рецензии: 28

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

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 !!!

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.

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26–32 из 32 отзывов о курсе Machine Learning Modeling Pipelines in Production

автор: Liang L

22 июля 2021 г.

Good content and hands on.

автор: Raspiani

28 авг. 2021 г.

Awesome Thanks

автор: 莫毅啸

24 дек. 2021 г.

haved fun!

автор: EMO S L

29 сент. 2021 г.

Nice !!!!

автор: Carlos A L P

3 янв. 2022 г.

G​reat course, you can learn new concepts related to MLOps and new technologies like major Cloud vendors, packages and platforms like TensorFlow for the ML model. I would like to have more exercises to apply the various terms and processes seen during the course

автор: Ruan L D

19 нояб. 2021 г.

Good but I think that is much content for low time

автор: Panagiotis S

25 янв. 2022 г.

Poor content on this course as well. A bit of intermediate machine learning concepts that we all have seen a thousand times and a bit of mlops. The instructor was always always reading only whats in the slide. Graded assignments on GCP were just copy/pasting the code and had no difficulty or needed any critical thinking or skills. Again focused ONLY on Tensorflow libraries that are incompatible with models from other libraries like Pytorch.