AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2 instances pre-installed with deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new…
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Google Cloud AI
Score 8.3 out of 10
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Google Cloud AI provides modern machine learning services, with pre-trained models and a service to generate tailored models.
Amazon AMIs has been very useful for the quick setup and implementation of deep learning for data analysis which is something I have used the service for in my own research. We commonly use the service to enable students to run intensive deep learning algorithms for their assessments. This service works well in this scenario as it allows students to quickly set up a suitable environment and get started with little hassle. If you are looking to run simple, surface level deep learning algorithms (kind of contradictory statement I know) then AMI is more complicated than most will need. When it comes to teaching the basics of Machine Learning, this kind of system is unnecessary and there are other alternatives which can be used. That being said this service is a must if you are looking to run complex deep learning via the cloud.
Google Cloud AI is a wonderful product for companies that are looking to offset AI and ML processing power to cloud APIs, and specific Machine Learning use cases to APIs as well. For companies that are looking for very specific, customized ML capabilities that require lots of fine-tuning, it may be better to do this sort of processing through open-source libraries locally, to offset the costs that your company might incur through this API usage.
Some of the build in/supported AI modules that can be deployed, for example Tensorflow, do not have up-to-date documentation so what is actually implemented in the latest rev is not what is mentioned in the documentation, resulting in a lot of debugging time.
Customization of existing modules and libraries is harder and it does need time and experience to learn.
Google Cloud AI can do a better job in providing better support for Python and other coding languages.
We are extremely satisfied with the impact that this tool has made on our organization since we have practically moved from crawling to walking in the process of generating information for our main task to investigate in the field through interviews. With the audio to text translation tool there is a difference from heaven to earth in the time of feeding our internal data.
So far we have not had any problems with the application, it is extremely easy to use and it is also easy to install. It does not require much training since the instruction is quite clear for its execution and application in the procedures of the company. The team in charge of this application is very pleased to work with this app.
Every rep has been nice and helpful whenever I call for help. One of the systems froze and wouldn't start back up and with the help of our assigned rep we got everything back up in a timely manner. This helped us not lose customers and money.
In fact, you only need the basic tech knowledge to do a Google search. You need to know if your organization requires it or not,. our organization required it. And that is why we acquired it and solved a need that we had been suffering from. This is part of the modernization of an organization and part of its growth as a company.
Both of these services provide similar functionality and from my experience both are top class services which cover most of your needs. I think ultimately it comes down to what you need each service for. For example Amazon DL AMIs allows for clustering by default meaning I am able to run several clustering algorithms without a problem whereas IBM Watson Studio doesn't provide this functionality. They both provide a wide range of default packages such as Amazon providing caffe-2 and IBM providing sci-kitlearn. My main point is that both are very good services which have very similar functionality, you just need to think about the costs, suitability of features and integration with other services you are using.
These are basic tools although useful, you can't simply ignore them or say they are not good. These tools also have their own values. But, Yes, Google is an advanced one, A king in the field of offering a wide range of tools, quality, speed, easy to use, automation, prebuild, and cost-effective make them a leader and differentiate them from others.
Artificial intelligence and automation seems 'free' and draws the organization in, without seeming to spend a lot of funds. A positive impact, but who is actually tracking the cost?
We want our employees to use it, but many resist technology or are scared of it, so we need a way to make them feel more comfortable with the AI.
The ROI seems positive since we are full in with Google, and the tools come along with the functionality.
