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

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Acerca del Curso

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

RG

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

TF

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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51 - 75 de 425 revisiones para Introduction to Machine Learning in Production

por Mindset N

16 de jun. de 2021

This course is very hands-on. It clearly teaches Machine learning beyond python notebook. I enjoyed this course and currently taking the second part of this specialization "Machine Learning Data Lifecycle in Production". Great content from Andrew Ng and Robert Crowe.

por Ibrahim Y

24 de oct. de 2022

This course is full of practical insights on how to tackle real-world machine learning problems. Specifically for me, I was working on a problem for a long time and after taking this course and applying the skills I learned, I was able to get a wonderful outcome.

por Shekhar S

30 de dic. de 2021

It's really refreshing to see the "behind the scenes" perspective on ML algorithm development. Although I have been working in Computer Vision for more than 10 years, I found Andrew's frameworks to think about the project lifecycle and data very useful. Thank you!

por Omar M A M A E

26 de sep. de 2022

This is really one of the most important courses I ever took! Andrew as always explaining in a very clear and interesting way. The course material are so useful, I have been working in the ML field for 2 years and I learned a lot of new concepts in this course.

por Carlos A L P

4 de nov. de 2021

Great theorical material to understand ML projects. The 1st (ungraded) lab exercise was not very clear though when playing with the front end and back end application, it would be nice to provide more information or tips on how to complete it

por G A

13 de abr. de 2022

Great course, concise but valuable insights on how ML is actually used in the real world and what problems we typically face when deploying ML to solve actual business problems. Looking forward to the upcoming courses in this specialization.

por Hernán Q

23 de jul. de 2021

It covers a lot of the real world problems data scientists find when trying to build machine learning solutions. Many of the best practices reviewed here are a common sense thing but having it wrapped toghteter here was really great !

por Nilesh G

26 de jun. de 2021

Deep learning courses are always best, cover all aspects in theoretical as well as more emphasize on practical knowledge which helps a learner ready for the real life challenges in Data science domain...Thank You Andrew NG and Team

por Varshaneya V

29 de ene. de 2022

This course gives useful insights about deploying machine learning systems in production from PoC stage. These insights are the same that an experienced ML engineer would have got in his/her practical experience in the industry.

por Iosif D

17 de oct. de 2021

Amazing introductory course that gives you the full scope immediately, as well as many theoretical details on each section. I expect the following courses of the specialization to dive into more technical things and frameworks.

por Paulo A A M

26 de jul. de 2021

Excellent course!! A new way to understand the key factors to master the Machine Learning lifecycle. This is much more than one course, this is an invitation to change our mindset through an exciting journey with Andrew Ng!!

por Martin H

5 de oct. de 2022

For someone not starting out with machine learning in production it is a good introduction and for someone with experience it can be good with another perspective on ml in production, just run the videos on 2x speed.

por aitha v

18 de oct. de 2022

I needed to reset my deadline and start my learning again from week1 but it say error and was unable to reset the deadline. Not happy with the support.

this is not about the course content but support for new users.

por Taku F

6 de jun. de 2021

The course was fairly compact and you would be able to finish each week lesson every day if you eager to do so. It was fun and educational. I loved the surprise in the last question of the optional quiz in week 3.

por Motilal R S

13 de jun. de 2021

Great course explaining concepts on ML lifecycle and deployment, especially touching topics like concept and model drift, monitoring models, error analysis, experiment tracking, pipeline and lineage. I loved it.

por ChenChang S

23 de jun. de 2021

This is a great introduction for how the mature machine learning product could be morph into mature products with multiple challenges. It helps me a lot for understanding how future AI industry looks like !

por Daniel Y

17 de dic. de 2021

This course would be very useful if you are ML-engineers, data scientists. However, this course does not teach you how to code. To code, you need to take Deep Learning specialization or some other courses.

por Xiaonan S

23 de sep. de 2021

Very practical materials and application-focused methodology! A lot of rule-of-thumb gathered from ML pipeline experiences. Clear definition on acronyms and mainly easy-to-follow non-technical guidances.

por Hector B

11 de jun. de 2021

Very valuable course for those who already have some knowledge on machine learning or AI applications. Very close to what systems engineering processes recommend, as when seeking ISO15288 compliance.

por cattaneo

7 de nov. de 2022

Excellent course. Happy to reconnect with Andrew after a few years to continue my ML journey.

Full of useful tips & tricks, but also deep thoughts on how the ML community should evolve its practices.

por Dr. F T

15 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.

por rahul g

5 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

por Nilay

12 de jul. de 2021

Introduces you to the basics of MLOps in a well paced mannar. Would request to add more examples of structured data sets, as many companies usually are dealing with the related problems.

por Somaye K

8 de ene. de 2023

I really enjoy participating in a great class like Andrew's class. It's full of useful and applicable points that I encounter during a real prj.

Thanks for sharing this asset with us :))

por UGENTERAAN A L M

5 de jun. de 2021

The content of this course has been especially useful for me. I wish there were more emphasis on the tools recommendation as well, but the theoretical knowledge was just fine. Thank you!