Chevron Left
返回到 Machine Learning Modeling Pipelines in Production

学生对 提供的 Machine Learning Modeling Pipelines in Production 的评价和反馈

158 个评分
27 条评论


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...


Sep 13, 2021

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

Oct 20, 2021

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.


26 - Machine Learning Modeling Pipelines in Production 的 31 个评论(共 31 个)

创建者 Liang L

Jul 22, 2021

Good content and hands on.

创建者 Raspiani

Aug 28, 2021

Awesome Thanks

创建者 莫毅啸

Dec 24, 2021

haved fun!

创建者 EMO S L

Sep 29, 2021

Nice !!!!

创建者 Carlos A L P

Jan 3, 2022

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

创建者 Ruan L D

Nov 19, 2021

Good but I think that is much content for low time