Chevron Left
Volver a Introduction to Machine Learning in Production

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

4.8
estrellas
1,772 calificaciones

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.

Filtrar por:

26 - 50 de 351 revisiones para Introduction to Machine Learning in Production

por Wenjuan C

20 de sep. de 2021

I had a great time learning with Andrew in this Introduction to Machine Learning in Production online course. In 3 weeks, Andrew walked me through each step in the machine learning project lifecycle and shared many best practice tips (from years of experience of his own), which I felt could be directly adopted and applied.  I especially appreciate Andrew’s emphasis on a data-centric approach and raising human-level performance. There are two valuable and practical suggestions to increase your machine learning model accuracy and contribute to a successful ML project, which have not been given enough importance in practice. As always, Andrew’s friendly, clear, and concise style and his capability to explain complex ideas with simple language made some of the seemingly intimidating subjects easy to digest. 

por Suneha K S

26 de sep. de 2021

This course acts as a very good guide in helping one improve the most important thought-process aspect of the project development, that's vital to work in the field of ML and AI. There are countless videos and tutorials on the internet today, that will help one learn the technical skills. But this course provides a shortcut to learn thought/project development process aspects which are usually gained only through years of experience. The best part was, all the concepts have been explained by taking a real-world use case, which makes the discussions non-trivial and practical.

por dodo r

13 de abr. de 2022

Found the course really helpful as it uses a lot of examples to better explain importance of some steps or their appliactions. I was surprised to see some issues that I worked on for weeks discussed here with a good way to address them and those were quite similar to what we did but took lot longer.

This course is really great to help you make better decisions based on some standard practices and guidelines.

por Stefano B

3 de jul. de 2022

Excellent content and explanations, as always Andrew Ng proves to be enormously valuable and effective in conveying concepts in an easy and very actionable way.

The course contains many practical tips that every ML team should know by heart. My experience of multiple years in the field suggests that this is not always the case, and that team would have a unique opportunity to improve adopting these concepts

por Brad D

15 de dic. de 2021

Andrew Ng presents a very thoughtful and insightful look at production issues in machine learning. His insights answer a lot of the questions I had after finishing a bootcamp elsewhere, although I might not have been prepared to understand everything he said if I had not taken that bootcamp.

His tone is very reassuring and intellectually stimulating, despite pronouncing all 'c's with a 'z' sound. :- }

por prom l

30 de ene. de 2022

A​ndrew Ng is one of the top instructors I have ever had in my life. He is extrodinarily clear and his thoughts are organized. The content was also very good. I have been building ML systems for years and he gave words and clarity to ideas I intuitively know, and pointed out a number of really solid new idea, allowing me to better communicat to my team.

por Panagiotis S

25 de ene. de 2022

Excellent course. As always Andrew makes the content of the course engaging and brings us new ideas and concepts. I would love to easy Andrew on the other courses as well because unfortunately the next courses were not so engaging. The content was delivered by just reading the slide and there only was slight explanation of more advanced concepts.

por Muhammad D

22 de jul. de 2021

I find this to be a very philosophical approach to Machine Learning, especially where Andrew NG poise questions that will make you rethink entirely the way you've previously approached ML Problems. The explanations are broken down in a manner that makes it so seamless to grasp. Thank you for this opportunity.

por Jaime A D

4 de ene. de 2022

Genial, es un gran curso, 100% recomendado. Es difícil, incluso en escuelas de posgrado, encontrar un curso que cubra estos temas en Latinoamérica. Los temas que se abordan aquí sin duda tendrán un gran impacto en los próximos años. ¡Gracias por democratizar el conocimiento y ponerlo al alcance de todos!

por Eagle S

3 de feb. de 2022

Very well structured course, informative, interesting, logical with clear examples, I have had a clear view of a life cycle of an AI project and tips and pitfalls, hands on opportunities to test step. Andrew always remind students their responsibility of being ethical, highly recommended.

por Abhilash G

10 de ene. de 2022

The whole specialisation is the best place to start if you are looking to productionize your machine learning models. The way they put forward each and every concept of MLOps life cycle will be a big eye opener, for people who are taking your machine learning models to production.

por Michael S

24 de ene. de 2022

The course goes through error analysis, experiment tracking, scoping and more concepts related to machine learning projects. Very well taught by Andrew Ng.

It is an excellent introductory course to ML projects management in a production framework and has my warm recommendation.

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 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 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

A​mazing 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 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.