Google Cloud AI vs. Keras

Google Images analysis model is a good one and I think is very useful in our case of detections. Speech AI is also a good one. I can only recommend Google Cloud AI API and the model for that second will be SpeechKit by Yandex both these tools have exceptional values one can utilise to enhance their projects.

Incentivized

I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.

Incentivized

• Smart reply and its AI suggestions make the organization think more carefully about their e-mail responses in Gmail. We were skeptical at first but it really works well for many instances.
• We do a lot of business and contracts in Western Europe and South America, so the translate solutions make this much easier for our banking paperwork.
• When we go to meetings or during a meeting, we often use the Google voice search to save time on research and filtering ideas or analysis.

Incentivized

• Implementing neural networks and deep learning models is easy with this.
• Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
• It has good integration with other devices like Android.

Incentivized

• Hard to find what to use - To find the right products, you need look closely at the details of each API, and find which suits your purposes. This can be easily fixed by creating a main page that details all of the products simply.
• Expensive - The API costs can quickly add up, especially during the setup process and as engineers figure out the usage of the API.
• No playground or training - There is a lack of an "API playground" or training sessions that could make onboarding engineers to this API much easier.

Incentivized

• I didn't face any issue so far.
• The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.

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.

Incentivized

I give 8 because although it´s a tool I really enjoy working with, I think Google Cloud AI's impact is just starting, therefore I can visualize a lot/space of improvements in this tool. As an example the application of AI in international environments with different languages is a good example of that space/room to improve.

The reason for giving this much rating. 1. It makes my job really easy and fast. 2. Strong community support. 3. Overall cost.

Incentivized

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.

Incentivized

Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

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.

Incentivized

Google's documentation for their AI and Machine Learning products is a bit more straightforward and still much easier to onboard into compared to the Azure Machine Learning and other AI products. Additionally, Google's Cloud AI products provide more comprehensive specific use-cases that are API-optimized, and easier to integrate into existing scripts and backends.

Incentivized

As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.

Incentivized

• Positive impact on ROI due to reduction in staff needed to build, deploy and manage a AI workload pipeline.
• Positive impact on the business by moving to OpEx without need for upfront CapEx investment.
• Improvement in time to analyze the data (structured and unstructured), increasing the business's ability to act based on AI results.

Incentivized

• It helped me in learning the basic concept of deep learning by having hands-on experience.
• It has helped us to implement our NN with very little time.
• It doesn't give you the whole power to customize your neural network. If you want that then you have to shift to TensorFLow

Incentivized