Production Machine Learning Systems
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8.1/10 (Our Score)Product is rated as #248 in category Machine Learning
In the second course of this specialization, we will dive into the components and best practices of a high–performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Courses : 28
Google Cloud Training
Courses : 28
Specification: Production Machine Learning Systems
44 reviews for Production Machine Learning Systems
4.3 out of 5
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Production Machine Learning Systems
Michael F –
wow gcp michael feldman
Hemant D K –
Artur K –
It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations
Raja R G –
Very informative on production systems….
Lloyd P –
The module on hybrid systems was weak. The time it would take to cover the material would be prohibitive so why do the intro that then apologize for not having the time to explain the material. Leave it out…
Mark D –
Very practical which was nice. Thank you for adding the Quicklabs that helped a lot.
bhadresh s –
It was bit hard course but lab work was great and learn many production level consideration for ml systems.
Alexander K –
Facundo F –
Rich course, although a little tedious, the info is priceless almost all the time. good for consultation
Cameron S B –
much meatier of a course.
Mirko J R –
Gregory R G J –
Armando F –
I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative
While there is definitely some good and useful content in this course, not all of the material is useful. 40% of the course felt like a sales pitch, at least to me.
Skander H –
Really informative and insightful.
Venkata P I –
very good information. Lot of unknown facts in ML are brought up in the course.
Kim J W –
SOYOUNG J –
Jakub B –
Subscribing to this course only gives you option to run assignments on Qwik labs, and they’re very poor for these kinds of assignments. You won’t get any feedback on assignments anyway since there is no grader. If you want to check out the material it’s better to just clone training data analyst from github and do these assignments on GCP free tier.
Jincheol W –
Junhwan Y –
This course include deep contexts about Machine Learning. But, It’s somewhat boring.
Lee M –
Mina J –
I walk through the whole system for the entire process of ML so that I could get insights on the forest
This specialization consists of 5 courses: Course1: End to End Machine Learning with TensorFlow on GCP Course2: Production Machine Learning Systems Course3: Image Understanding with TensorFlow on GCP Course4: Sequence Models for Time Series and Natural Language Processing Course5: Recommendation Systems with TensorFlow on GCP In specialization’s FAQ say nothing about “audit” option. Are You know what is it ? “Audit” means that You can use course video material even after You subscriptions ended. By fact, only “Course 1” has such ability. Before pay for specialization, carefully check FAQ for EACH separated course in specialization: courses 2 5 has special items in FAQ: “Why can’t I audit this course? This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available. ” “Who have paid” means that after You subscriptions ended, you lost access to video materials in this courses. p.s. 1 star only for “Audit”, content and lecturers are rated higher at least 4 stars
Kate K –
Really useful course
Suresh R –
ANDRE L M P –
Steven P G –
Es un curso algo confuso que requiere bastante tiempo para comprender las tematicas
Nikhileshkumar I –
Great. Ksonnet is not active. Vdo should talk about it.
Daniel E –
This course, I believe, will be crucial to my future understanding of end to end ML applications. I only hope gain more practice in assembling all the pieces from experimentation, to model design, to data engineering.
Mr. J –
direct to the point practical guidance for dev stage prod sequence
Sakti D –
Prasenjit P –
Sachin T –
This was a great opportunity to learn the Production Machin learning system.
Alireza K –
The Qwiklabs should be more than copy pasting commands. Also I think this course is suitable for people with many years of experience in software development not people like me just came out from university!
M T –
The first module was really good, but the others just seemed like an ad for GCS. Also, the 3rd and 4th module the labs / lab video was hard to follow and felt like I was just reading random code.
Muhammad W P A –
very good for people who will enter the production stage on machine learning systems
Mahendra S C –
Awesome course for Production of machine learning mode.
Melissa K R –
I was really hoping I’d gain some real practical skills and knowledge about the different aspects of building and deploying a machine learning model on GCP. Even though a lot of real estate was covered in this course, most of it was theoretical, and I cannot say that I “really” learned how to implement them if I were working on a big machine learning project, which was exactly why I took this course. The only labs that had some practical aspects to them were also disappointing; one only looked at a number of Java modules and the other was a demo of Kubeflow that I couldn’t follow at all, and was different from the lab itself! First off, the fact that Java was used in the first instance took me by surprise, and I wonder why the same thing couldn’t be accomplished with Python. I have zero knowledge of Java and that was uncalled for, but tried very hard to make sense of the code. But I won’t definitely be able to write it myself. And in the case of the last demo, I simply couldn’t understand what the instructor was doing and where! I expected a much much much higher standard from this course, but overall it was quite disappointing and I cannot say I took anything away from this course other than some theoretical concepts about various subtleties when it comes to ML on GCP! It would’ve really really helped if there were more actual lab work included in this course, just like the previous one and each concept was accompanied by one such hands on lab, and concepts were explained step by step. The other thing that was very odd to me (and is the same for every other course in this specialization) is that a ton of material is squeezed in Two Weeks. It would’ve helped if they were separated over multiple weeks. This change in the organization of material would really help learners to visualize the flow of topics. Right now, it all seems a load of crammed topics that have been merely glossed over!
VIGNESHKUMAR R –
ghouse g –
Really focuses on topics for building production ML Systems