Amazon Forecast is a fully managed service that uses machine learning to deliver accurate forecasts. Amazon Forecast can use historical time series data (e.g., price, promotions, economic performance metrics) to create accurate forecasts for businesses.
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Google Cloud AI
Score 8.4 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.
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SAP Predictive Analytics
Score 7.0 out of 10
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SAP Predictive Analytics is, as the name would suggest, a statistical analysis and data mining platform that can be deployed with SAP HANA.
Amazon Forecast is well suited when you are a company that's looking for a simple and effective solution in terms of understanding and predicting your resources planning in the AWS. However, it's also good to know that the cost that is incurred is higher and not suited for anything other than the AWS solutions integrations.
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.
It's a great tool to merge actual data analysis (which Lumira doesn't do that well) with visualization (which Lumira does well) - so it can be seen as Lumira for data analysts. However, a lot of the 'predictive' side is hidden/black box which can be frustrating for those analysts, so you could argue it is too complex for casual users, but too 'black box' for analysts.
It doesn't require you to have a Ph.D. to build models!
You can use it to address a very large and wide dataset without worrying about sampling.
Automation is in the product DNA. You can prepare your data, ingest it into the "Kernel", then get insights about what was found, decide to publish it and schedule scoring tasks or model refresh in the same product.
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.
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.
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.
The documentation provides an explanation about what features are available but not necessarily what's happening behind the scenes. On the other side, the "community" has grown since the acquisition and most questions are properly addressed by SAP folks. Since the "product maintenance" mode announcement was made, there wasn't much new content published except on the Smart Predict side (which is built by the SAP Predictive Analytics team)
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.
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.
We have typically used Spotfire for data analysis but decided to move to SAP Business Objects due to its innate connection with SAP. I found Lumira to be good for visualizations but it is not meant for data analysis. Therefore, we have introduced Predictive Analytics to see if it can fill that gap. So far, it's been far less intuitive than Spotfire to get started, and as far as I am aware so far, it does not bring many additional capabilities. I do, however, like that it utilizes the Lumira look/feel and integrates very well.
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.
