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Opiniones y comentarios de aprendices correspondientes a Introduction to Machine Learning in Production por parte de

1,774 calificaciones

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In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. 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: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline...

Principales reseñas


4 de jun. de 2021

really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value


14 de ago. de 2021

Excellent course, as always. Very well explain for both Data Sicientist, Software engineer and Manager (with some basics undertsanding of ML). One of these courses that Data Sientist should follow.

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151 - 175 de 351 revisiones para Introduction to Machine Learning in Production

por Zoran H

25 de jun. de 2022

It is a nice introduction to MLOps segment general terms and background.

por Ghada S

11 de ene. de 2022

Very insightful course. Many thanks for all the provided efforts over it

por Saqueeb A

20 de abr. de 2022

Learned some basics of MLOps and terms in this course. Looking forward!


20 de sep. de 2021

This Course covers all the basic practical aspects about ML Production.

por Juan A

19 de may. de 2021

Very Excellent Course. Allows me to seek and dig deep into the ML World

por Aymen S

7 de jun. de 2022

The is a very good introduction course for this amazing spezialisation

por andre m

14 de jun. de 2021

Amazing course, theory based on experience. Lessons that no book have.

por Hoang V

6 de jun. de 2021

O​nce of the best! I learnt a lot of useful insights from this series


1 de ago. de 2021

Another great specialization. Grateful to whole team

por Rob B

3 de jun. de 2021

G​reat course with high-level information not covered anywhere else.

por Ryan C

4 de feb. de 2022

Great content, shame the video of Andrew NG talking is stuttering.

por Cayo S

5 de oct. de 2021

G​reat introduction to key concepts and practices in production ML

por Raspiani

1 de ago. de 2021

Great, Awesome, Wonderful, Fantastic, Thank You So much Prof.. :)

por Stefan L

5 de ene. de 2022

Great introductory course that touches a lot of important topics.


30 de sep. de 2021

Awesome course for college grads to get a head start in Industry.

por Justas K

27 de may. de 2021

High abstraction level with toy examples on very complex things.

por aadarsh a

27 de ene. de 2022

this is the best machine learning course which i have seen ever

por Mauricio R

10 de nov. de 2021

Outstanding course, great depth and breath of ML in production

por Faruch A

29 de oct. de 2021

O​ne of the best non-academic course I have ever experienced.


25 de jun. de 2021

Good explanations of the principal problems in the ML project


19 de may. de 2021

Great course! Easy to understand with many examples explained

por Nikhil S

29 de abr. de 2022

Awesome Course, full of insightful knowledge from Andrew Ng.

por Apoorv A

9 de mar. de 2022

W​ould have loved a programming assignment with this course.

por GB L

27 de jun. de 2021

Nicely introduced the MLOps and illustrated some real cases.

por Lộc Đ

2 de ago. de 2021

It's time to say thank you for this course and Mr Andrew Ng