Google Cloud ML Engine
von Google4.6 / 5 74 Bewertungen
Durchschnittliche Bewertung74 Bewertungen
- Gesamt 4.6 / 5
- Benutzerfreundlichkeit 3.9 / 5
- Kundenservice 4.3 / 5
- Funktionen 4.6 / 5
- Preis-Leistungs-Verhältnis 4.8 / 5
- Kostenlose Testversion Nein
Angaben zum Hersteller
Über Google Cloud ML Engine
Managed service for creating ML solutions. Provides ML model building and training, predictive analytics, and deep learning.
Google Cloud ML Engine Funktionen
- Deep Learning
- Prädiktives Modellieren
- Statistische / mathematische Werkzeuge
Die hilfreichsten Reviews für Google Cloud ML Engine
Bewertet am 14.11.2019
Google Cloud ML Engine review
Kommentare: In constructing ML project at first, it is run by the local hardware platform Tensorflow GPU version, so that at the time of training can speed up a lot, but because of the high cost of GPU, when a project order of magnitude increases, the training time of exponential growth, if want to reduce the time, only through optimization algorithm or hardware.After that, I moved the whole project to the cloud platform for operation. Of course, there was also a problem. The resources of Aliyun were all based on fixed configuration to determine different prices.Finally, I migrated to Google Cloud ML Engine, which was cheap and perfectly compatible with other Google products, such as Cloud Storage, Cloud Dataflow, and Cloud Datalab.For the extension of the project and late derivative, provides a great convenience.
It doesn't take up any of my local computer resources, just throw a command and let the Google cloud run when I need to run, and it doesn't block any of my other work.
The software provides mainstream training model, prediction model, mainstream ML framework to accelerate the efficiency of our project development.
Low price, suitable for early learning and research.
Nachteile: The threshold of software use is relatively high, and the background of Python or Tensorflow is required, so it is difficult to get started.
Bewertet am 15.2.2019
popular machine learning platform with great functionality
Kommentare: trying to get a jumpstart on a machine learning platform - preferably open sourced since there will be a community and we can learn/contribute along with community.
Vorteile: multiple GPU support availability, ability to queue data prior to loading for graphs, ability to visualize graphs during the process of data modeling to enable faster debugging and logging events on tensorboard for quick sanity checks are some of the stand out features.
Nachteile: at times, we run into an issue when dealing with variable length sequencing inputs in recurring neural networks. Also puts a strain on performance and memory when dealing with a large data set and slows down.
Bewertet am 27.9.2019
Relatively Straightforward Deep Learning Framework
Kommentare: Human pattern recognization, image recognization. Habits and trends.
Vorteile: The 2.0 version is easy to set up and there are a lot of APIs that are integrated for using various programming languages to do the same thing. I personally have been using python with this application and have had very little problems getting going. There are a lot of tutorials on getting started, some good data available for free to assist with the learning process. Everything can be run locally which makes it easy to expand on-site. Cloud options are also affordable.
Nachteile: The learning curve is a bit steep. This isn't specifically an issue because of TensorFlow itself, the idea of neural networks are not simple. TensorFlow has made improvements on 2.0, that make it easier to use compared to previous versions.
Bewertet am 27.9.2019
ML must have
Kommentare: Definitely my first option when neural networks are involved in my personal and professional research. Furthermore it also has High-Level API (Keras) which make everything easier.
Vorteile: Mastering tenworflow unlocks in you all the possibilities you can have with the current Machine Learning techniques. Data Science, Computer Vision, Machine Learning, NLP...name any area of research of AI, tensorflow can easily handle it.
Nachteile: It might be a little too complicated at first, especially if you are a beginner of Neural Networks. But sticking with it will give you the ability to interact with it.
Bewertet am 7.3.2018
Tensorflow is the future of our business, and likely the future of machine learning modeling.
Kommentare: Tensorflow is the future of machine learning modeling. There is no way around that and we as a company are fortunate to bring this technology to the forefront.
Vorteile: Tensorflow is the easiest way to implement machine learning software into your product/business. The repository is colossal and there is an abundance of support within the community alone. Tensorflow is updating regularly and will continue to grow in the years to come.
Nachteile: Hardware is a common bottleneck in machine learning software. We have built out dedicated computing space just for our tensorflow models and will have to continue to upgrade and expand that space. It's just the nature of the business.