Google

Google Cloud ML Engine


5 / 5
35 reviews


Average Ratings

35 Reviews

  • 5 / 5
    Overall

  • 4 / 5
    Ease of Use

  • 4.5 / 5
    Customer Service

Product Details

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About Google Cloud ML Engine

Managed service for creating ML solutions. Provides ML model building and training, predictive analytics, and deep learning.


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Google Cloud ML Engine Features

  • Deep Learning
  • ML Algorithm Library
  • Model Training
  • NLP
  • Predictive Modeling
  • Statistical / Mathematical Tools
  • Templates
  • Visualization

Google Cloud ML Engine Reviews Recently Reviewed!


Tensorflow is the future of our business, and likely the future of machine learning modeling.

Mar 07, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: 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.

Cons: 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.

Overall: 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.

Gold standard for ML libraries

May 02, 2018
5/5
Overall

3 / 5
Ease of Use

4 / 5
Features & Functionality
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Pros: This is the industry leading machine learning library. It's essential for any deep learning models you're looking to implement. The repository is large and very thorough. We've trained many datasets on various models using tensorflow and couldn't be happier.

Cons: It's not easy to use. This is the case for most emerging technologies though, the learning curve is dramatic but such is the cost of new tech.

Overall: Tensorflow is a one stop shop for most machine learning applications. The ease of use isn't really there but once you learn the processes required, everything falls into place. We use it to train various machine learning models and couldn't be happier.

Capterra-loader

A Machine and Deep Learner must have Library

Sep 04, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: This Library is very flexible for doing Matrices and Tensor So building very deep high level but quick and scalable ready to use neural networks is at your finger tips.

The added other Anaconda Library and Keras compatibility

Cons: Depreciation of the code is frustrating. To use one form just to throw a Error message.

Capterra-loader

TensorFlow is useful, although it requires a healthy time commitment to produce accurate models

Apr 26, 2018
4/5
Overall

2 / 5
Ease of Use

5 / 5
Features & Functionality

1 / 5
Customer Support

5 / 5
Value for Money
Likelihood to Recommend: 7.0/10 Not
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Pros: TensorFlow is fascinating in seeing how it produces results in a reasonable time frame. It is completely flexible compared to its costly competitors. The software connects well with various data sources and in setting up scripts to run automatically.

Cons: TensorFlow takes a lot of time to become an expert in what it is doing. The programming time-commitment might not be worth it unless you plan on customizing your modeling to work with other software.

Overall: The benefits I received from this software is more accurate modeling and an interesting insight into what makes one software better than another. TensorFlow did for me what it says it does - produce high quality models, such as neural networks, with a lot of human capital input.

Capterra-loader

Very helpful in the new world of machine learning.

May 11, 2018
5/5
Overall

2 / 5
Ease of Use

5 / 5
Features & Functionality

4 / 5
Customer Support

4 / 5
Value for Money
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Pros: I used TensorFlow on AWS which was easier with all the infrastructure AWS built. It was a good start to machine learning with all the AI and neural network popularity going on these days.

It was challenging and exciting to prepare datasets, train them and see the satisfactory results in dashboard.

It is also open source and this gives an advantage to TensorFlow.

Cons: There is a long and challenging learning period. Documentation is rich but it would be so much better to learn and use it with some visual aids.

Overall: You will learn a lot from TensorFlow. It is a good way of entering the machine learning world.

A powerful high-level machine learning library!

Apr 19, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money

Pros: Tensorflow is a high-level machine learning library. I can use it to design neural network structures without writing C++ or CUDA18 code in order to get high efficiency. It supports automatically calculating derivative. Tensorflow is implemented with C++ and it uses C++ to simplify online deployment. In addition to C++ interface, it also provides us with Python, Java and Go interfaces.

Cons: Although Python is very powerful and easy to use, using Python with TensorFlow will still cause some efficiency problems. For example, every mini-batch needs to be fed from Python to the network. During this process, when the data size of mini-batch is small or calculation time of is short, it will cause long latency.

Capterra-loader

Most advance machine learning library

Sep 03, 2018
5/5
Overall

4 / 5
Ease of Use

4 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: I think it is the most advance library for machine learning specially for deep learning. It very easy to write neural network in this library. It comes with lot of inbuilt function to process data. Also, it has lots of prebuilt function which ease the implementation of neural network.

Cons: There is no bad thing about this but initially it takes lot of time to understand it as it works on tensors instead of simple vector or array object. But once you learn this, it will be easy to write code.

Overall: Building machine learning model from scratch and want full power of customisation then choose this tool.

Capterra-loader

Made the deep learning kids work

Jul 11, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: This library is the best for deep learning. Designing neural network with this library is very easy. Also, it compute things very fast. It has made the visualization very easy. It has lots of built in features like conv2d network, lstm etc.

Cons: It's the best thing to do stuff in deep learning but it require a long learning curve. But once you know how it works then it made your job very easy.

Overall: Best library for deep learning

Rapid prototyping of neural network models which is a great learning resource for students

Mar 29, 2018
5/5
Overall

4 / 5
Ease of Use

4 / 5
Features & Functionality

4 / 5
Customer Support

5 / 5
Value for Money
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Pros: Has rich resources to support and help in learning the nuances of Neural Networks and Deep Learning and helps in rapid initial prototyping

Cons: Has a steep initial learning curve and is not high level programming system like Pytorch. Would require more effort in defining the the different modules of the project.

Overall: Rich community support and learning resources.

I adore this

Sep 26, 2018
5/5
Overall

4 / 5
Ease of Use

4 / 5
Features & Functionality
Likelihood to Recommend: 8.0/10 Not
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Pros: Great way to have all in one place- cakendar,docs,calculations. It makes my work do much easier and convenient.

Cons: It has all I need in one place,so no flaws

Capterra-loader

Deep learning is easy with TensorFlow

Sep 06, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality
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Pros: Intuitive way to generate networks.

Nice visualizations with tensorboard.

Good documentation and tutorials.

Large supportive community.

Easily scaleable.

Cons: Many of the error messages can be cryptic and difficult to use when debugging.

Frequent errors caused by data type mismatches.

It is not easy to iterate quickly with Tensor flow.

Overall: We use tensorflow LSTMs for sequence classification to mine patterns in log and customer behavior.

In our use case, deep models decreased testing loss by 50% over a simple baseline.

Deep Learning Musthave

Mar 16, 2018
4/5
Overall

3 / 5
Ease of Use

4 / 5
Features & Functionality

4 / 5
Customer Support

4 / 5
Value for Money

Pros: useful for debugging complicated computational graphs when combined with subgraph execution. Can run and train deep neural networks

Cons: would be better if released with RPC and distributed implementation. Latest version were not good in performance category. Time to deployment is very much

Capterra-loader

Tensorflow is the best open source library for machine learning framework

Jun 29, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality

4 / 5
Customer Support

5 / 5
Value for Money
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Pros: Some of the pros are as follows:

1. Compatible across various platforms like GPU, CPU and TPU.

2. Better computational power, performance and graphical visualization than Theano, Torch etc.

3. Keras could be used as backend for the same.

4. It could be used on various devices from cell phones to powerful supercomputers.

Cons: Some of the cons are as follows:

1. Lack of Symbolic loops (which is given in Theano and Caffe)

2. Lack of support on Windows.

Overall: Helped me to create various machine learning models

Capterra-loader

The Rising star for the Deep Learning...

Jun 12, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality

4 / 5
Customer Support

5 / 5
Value for Money

Pros: - Unintuitive design

- Computational Graph can be executed immediately

- Development is extremely rapid

- Python Numpy support Yaay

- Better for Deployment

- Don't forget about the Google's DeepMind, Google Brain, Toronto, IDSIA etc

Cons: - Documentation can be more refined

- Can hog a GPU

- For practical ML Tasks other than Deep learning, I will still prefer Scikit learn

- Overkill for simpler tasks

- Not enough pre-trained models for practical use

- Slower than some of the frameworks

Capterra-loader

Writing deep learning algorithm is very easy -- very advance machine learning library

Jul 15, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: Implementing deep learning algorithm is very easy. It has lots of built in feature like conv2d, conv3d, lstm etc. which make ML work very easy and faster. Computationally, it is way faster that other ML libraries.

Cons: It take time to learn about this. Understanding the tensor and other data type is non-trivial. But once you learn this it's very easy to use it .

Mr

Apr 28, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: I love how it makes Deep Learning simpler by removing the need to maintain VMs. Tensorflow is an amazing framework that does pretty much everything in deep learning.

Cons: Tensorflow has a steep learning curve. More tutorials may be provided for beginners to get them on board.

Overall: Saved a lot of time by removing the need to maintain VMs.

It was easy to build machine learning models that can work on any types of data and size.

Jan 13, 2018
4/5
Overall

5 / 5
Ease of Use

4 / 5
Features & Functionality

4 / 5
Customer Support

4 / 5
Value for Money

Pros: 1. Cost Efficient (Pay as you go)

2. Provide easy way for accessing data and manipulating cloud API to automate Scalable operations

Cons: Calls Per request is bit high. Would be happy if they provided more calls in the free version. Rest it's best in class service.

Good Features and Learning

Mar 16, 2018
4/5
Overall

4 / 5
Ease of Use

4 / 5
Features & Functionality

4 / 5
Customer Support

4 / 5
Value for Money

Pros: They have visually appealing components. Support from google is best feature that tensorflow have. It supports wide varierty range of operations.

Cons: current open source implementation does not support distributed computaion and windows support is not present. In future update more guide for new users should be added.

Tensorflow has a rich framework for your machine learning problems.

Feb 16, 2018
4/5
Overall

4 / 5
Ease of Use

4 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money

Pros: The best part about tensorflow is that it is open source software so anyone willing to contribute can easily do

Cons: The framework is a little bit of bloat and can be a little tough to get started with. Apart from that it does well to provide for basic machine learning and deep learning tasks.

Nice ML engine

Apr 12, 2018
4/5
Overall

1 / 5
Ease of Use

4 / 5
Features & Functionality
Likelihood to Recommend: 7.0/10 Not
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Pros: Very, very fast - you are able to use GPU implementation of some layers of Keras on TensorFlow, it is easy to use with Theano

Cons: Very hard to start, it will be great to have much more tutorials. And it will be usefeull to have some more trainings available.

Capterra-loader

The best deep learning library

Jun 25, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: It is best library to wirte models for deep learning. One advantage is that it is open source. One of the best thing is that, now it has lots of pre built neural network architecture in it.

Cons: This library requires a long learning period, understanding everything in this library is not very easy.

Overall: I have been using this for one and half year, and it's a good learning. And also efficient to build deep learning models.

Capterra-loader

Excellent Software

Sep 12, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: User friendly.

Great features and functionalities.

Availability of tracking bug.

The software shows CPU usage.

Cons: The CPU usage is only available in percentage.

If you want to visualize your deep learning , this is the place to visit

Mar 12, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

Pros: It is essentially the best machine learning and deep learning software. You get to visualize what you are doing with their dashboard visualizer which is basically google analytics for deep learning. I fell in love with Deep learning because of tensorflow.

Cons: I had a little difficulty with setting up the working environment as it acts as a server and shows the visualization in the browsers local host.

Capterra-loader

One of the 2 most updated deep learning frameworks

Jun 27, 2018
4/5
Overall

3 / 5
Ease of Use

5 / 5
Features & Functionality

3 / 5
Customer Support

5 / 5
Value for Money
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Pros: They keep adding all new paper and research state-of-the-art ideas, which is a key feature for a deep learning library.

Cons: Versions are not back-compatible. This is a huge issue when trying to maintain a code that was created on top of it.

Google Cloud ML Engine is easy to use and the API is best.

Apr 18, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
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Pros: I like the amount of storage you receive to store finished training modules.

I also like the guidance given throughout the wizards to help ensure your finished product is awesome.

Very easy to sign up and use.

Cons: Time consuming.

Security implications of hosting critical enterprise applications on a public cloud.

Big learning curve.

Deep dive into machine learning capabilities

Oct 07, 2018
5/5
Overall

2 / 5
Ease of Use

5 / 5
Features & Functionality
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Pros: Lots of algorithms, models and example can be found and used in order to develop a machine learning solution. Everything is managed in order to optimize performance and boost your work towards a working solution.

Cons: A bit of programming experience is required in order to understand the implemented lines of codes.

Tensorflow is the one stop solution to all your machine learning needs.

Feb 27, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money

Pros: tensorflow has been frequently updating and improving its api's on a regular basis and has turned into a huge repository of machine learning tools.

Cons: Requires extensive hardware to run effectively and much better integration with third party apps can be provided. Please provide better documentation.

Great if you know what you are doing

May 11, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality

Pros: TensorFlow is an incredibly valuable and simple way to construct neural networks and do data analysis and prediction. Very straightforward and functional.

Cons: The learning curve is obviously steep. Some of the error messages can be tough to decipher without outside help.

Great Experience

Aug 16, 2018
5/5
Overall

3 / 5
Ease of Use

4 / 5
Features & Functionality

4 / 5
Customer Support
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Pros: Best thing about it is the training and prediction services.

Cons: Sometimes it crashes and completely blacks out

Open source and Well Maintained

Mar 16, 2018
4/5
Overall

3 / 5
Ease of Use

4 / 5
Features & Functionality

4 / 5
Customer Support

4 / 5
Value for Money

Pros: Rapid deployment, models can be deployed easily. It is well maintained and keep updating.It is open source and community support is good

Cons: Control flow operations and loop functions are missing. Windows support is not there.Distributed training is not supported.

Google Cloud ML engine is very helpful for beginners who are learning and working with Tensor Flow.

Mar 08, 2018
4/5
Overall

3 / 5
Ease of Use

4 / 5
Features & Functionality

3 / 5
Customer Support

4 / 5
Value for Money

Pros: * Easy to get started.

* Free subscription for beginners.

* Helps in applying various service provided and get hands-on ML experience.

Cons: * Special service for students can be provided.

* More options can be provided to integrate with existing applications.

Awesome product. Lets you scale millions of records on cloud in no time

Apr 24, 2018
5/5
Overall

4 / 5
Ease of Use

5 / 5
Features & Functionality

4 / 5
Customer Support

5 / 5
Value for Money

Pros: Lets you train your data on the Google distributed cloud which can scale millions of records. It is very helpful for machine learning enthusiasts. Generally, people use laptops to train data which has thousands of records. But we can't scale more than a thousand records on our laptop. That is where this product plays an important role.

Cons: There is not a lot of cons about this product. Just the UI can be simplified for the better understanding of the user. Otherwise, it is an awesome product

Nice library

Oct 11, 2018
5/5
Overall

5 / 5
Ease of Use

4 / 5
Features & Functionality

5 / 5
Customer Support

4 / 5
Value for Money
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Pros: I liked the both CPU and GPU integration of Tensor flow.

Cons: FPGA integration should also be added. ML in FPGA is growing filed these days.

The most advanced ML platform - accessible to all

Mar 13, 2018
5/5
Overall

5 / 5
Ease of Use

5 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money

Pros: Industry-leading ML tools and algorithms, with top-notch documentation and courses to help even beginners get started easily.

Cons: Could use some more pre-defined templates for common problems, to help with not reinventing the wheel.

Decent option for simple ML

Aug 05, 2018
4/5
Overall

3 / 5
Ease of Use

4 / 5
Features & Functionality
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Pros: It has a decent range of ready-to-use machine learning algorithms which performance can be tracked in a simple way.

Cons: For an advanced machine learning practitioner, the capabilities are somewhat limited