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Avis et commentaires pour d'étudiants pour Introduction to Machine Learning in Production par deeplearning.ai

4.8
étoiles
1,562 évaluations
273 avis

À propos du cours

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...

Meilleurs avis

RG

4 juin 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

IU

5 déc. 2021

I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!

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1 - 25 sur 310 Avis pour Introduction to Machine Learning in Production

par Francisco R

21 mai 2021

I know it's an introduction, but I got a bit disappointed. It's quite basic and even though it has some hands on notebooks, they're optional and you don't need to work on anything. Quizzes are easy, and I didn't have the feeling I learnt much. I'm still rating it with 3 because, well, it's Andrew Ng, and this his teaching is worth gold.

par Snehotosh B

22 mai 2021

I found the production part absent and is another ML course.

par Rajesh R

22 juin 2021

Most of the discussion was theoretical. Some useful knowledge but not useful for real world MLOps

par Kyung-Hoon K

16 juil. 2021

Thanks, Andrew!!!!! Your sharing real-life experiences was invaluable. This was super special as it has opened my eyes beyond the ML-code. I've realized what I have to do in my real job. I will spend more time on communicating with business teams to close the gaps on different metrics expectations. I will shift my mindset from code-centric to data-centric. I will check out my data before my team dives into the ML coding itself. Thanks, Andrew and the team!!

par Gaurav G

23 mai 2021

Awesome Course.... :) Really I enjoyed a lot. I completed this 3 weeks of course just in 4 days along with my office work (too much interesting).Very helpful... Very knowledgable... Thanks Andrew Ng for the course. A big thank to DeepLearning.AI team.

par Mohamed A H

14 mai 2021

I give you the full review stars since I learned many new things that I did not pay attention to before, e.g.: I used to focus on models for many years instead of data.

par Picioroaga F

13 juil. 2021

This course was one of best that I've taken regarding the ML. I think this course should be the starting point for each student who would like to pursue a career in ML and AI. Understanding the problem in the business context before jumping to the solution, understating the data in the same context, are the key ingredients for defining the success of a "product/service" involving AI.

par HARI A K

16 mai 2021

Really good for anyone with strong background in DL and ML... And want to be able to start a real time project... Or lead a ML team

par Koke H

3 juin 2021

All pretty trivial

par Omar A

20 juin 2021

I liked how Andrew is able to simplify difficult and tricky concept without making you feel uncomfortable about lacking the knowledge. Everything is smooth and up to the point. In addition, the labs are interesting and highly related to the material. Overall, the concepts taught are very helpful and important to make you an real machine learning engineer not just a one who copy and paste bunch of theories, codes, ....etc.

par Bhargav U

30 sept. 2021

If you have work on industry projects, you must have come across such scenarios described in the course. This course provides a structured way to analysis different situations arises during a ML project life-cycle and teaches way to make decisions which increases the chance of success. It is really helpful.

par Cristiano G

11 juin 2021

Very nice course! The field of MLOps is not so well documented and fortunately we have very experienced professionals able to share their expertise. The content is very clear and the examples provided by the professor are extremely insightful.

par Dennis D

21 mai 2021

Even after having worked several years in the role of an MLE there were some useful ideas here and there that I'm excited about applying in the future. Overall, everything was very clear and understandable. I liked the lab about deployment.

par Wesley E B

16 mai 2021

It had some great advice for how to design a machine learning system. More practical examples would have been appreciated.

par Rawan L

9 juil. 2021

Very basic course

par Keith K

5 juil. 2021

I found that the course is quite useful and practical. I enjoy a lot watching Andrew's Lectures especially when he used many examples from his previous projects in his career , giving good demonstration of common challenges in ML model development as well as maintenance/monitoring in production. The course is well designed and gives us a very clear foundation about Machine Learning in production.

par Tamim-Ul-Haq M

13 juin 2021

Incredible course. It describes in detail of how machine learning engineering is done in a production environment. It takes the aspects learnt for Course 3 (Structuring Machine Learning Projects) from the Deep Learning Specialization (also taught by Andrew Ng) and provides an even more in-depth knowledge base

par Engin K

11 sept. 2021

T​he course goes through methods to solve common operational problems that data scientists experience all the time but are not either aware of the problem or do not know how to solve the problem. All the methods are explained clearly with some practical examples. One of 'must take' courses by Andrew NG.

par Baturalp M

30 mai 2021

Great for beginners but I also ejoyed it since it nicely tidies the practical knowledge that an experienced ML engineer/data scientist gains throughout his work. Overall, it's a good polishing over my knowledge and learned some new points that I didn't paid enough attention to.

par ismagil u

5 déc. 2021

I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!

par Anand V S C

9 juin 2021

I have been working in a large payments technology company for last one year and I can vouch for all the processes Andrew beautifully summarised. It does help a lot working in the industry.

par Deepak K

14 mai 2021

it was good to learn

par Tigran M

20 août 2021

do not meeting the expectations

par Antoine C

23 nov. 2021

Not enough hands on experience

par Wenjuan C

20 sept. 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.