An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
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SAP Conversational AI (discontinued)
Score6.8 out of 10
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SAP Conversational AI was a platform used to build chatbots and digital assistants in SAP integration. Starting January 2023, SAP Conversational AI, SAP’s chatbot building platform has been set to maintenance mode. Existing customers can continue to use the enterprise edition of the product until the end of their contract.
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TensorFlow
Score7.6 out of 10
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TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
Senior Consultant in Information Technology at A.T. Kearney (Management Consulting, 1001-5000 employees)
Chose H2O.ai
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Founder in Information Technology at Denk Development (Computer Software, 1-10 employees)
Chose SAP Conversational AI (discontinued)
SAP Conversational AI is superior to rule-based solutions because they cannot understand a plurality of words such as synonyms. Rule-based systems may for example search for trigger words like "invoice" or "receipt" and recognize those just as well as the neural (?) models used …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose SAP Conversational AI (discontinued)
Easy development on it and great integration with other platforms.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It would be most suitable to help you attain swift conversation flows as you engage with your audiences. The bots are also of indispensable value in handling repetitive tasks around the firm such as automated HR resourcing expeditions or marketing campaigns or any other important but monotonous tasks. I however admit that analyzing the bot's performance is quite complex, have an RPA specialist around.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
SAP has helped me manage my teams cost in material management.
SAP Conversational AI has provided with the goal of developing bot analytics to respond to common user face issues when reporting troubleshooting issues with software equipment as well as technical equipment.
Has helped deploy new bots to increase response time to employees who require assistance with ordering equipment software as well as application development in software ordering.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
SAP can certainly provide better and more clear documentation on how to customize and deploy the chatbot/use SAP Conversational AI.
There is lot less developer community around SAP Conversational AI so it is hard to get help from outside developers and experts on best practices, hacks and existing applications/integrations.
It is hard to use SAP Conversational AI outside SAP S/4HANA Cloud, for example on AWS or GCP or in a multi cloud environment.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
Chatbots have already acquired most of the market and are still trending with the needs of changing market everyday. It will keep evolving with AI and NLP more to offer for improvements. SAP CAI is a good product to add to an enterprise using SAP ERP Suite
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Never had an issue. SAP CAI shares the same platform as any other product hosted on SAP Cloud Platform (aka BTP) and depends on your hosting (US, Europe, Asia). Maintenance modes are planned and customers are aware of it well in advance in order to mitigate potential impacts on the service offering.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It's a pure SaaS platform hosted on SAP Cloud Platform (aka BTP). The experience is pretty much seamless with minimum loadings or noticeable lags. The hosting depends on your location so you may want to make sure the instance is available on a server close to you, such as the USA, Europe or Asia.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Great support from the people of SAP Conversational AI as of the community. Sometimes it takes a little while for folks of the SAP Conversational AI team to answer but this has mostly to do with the overload of questions and users the product has. The gold-support channel within Slack that SAP Conversational AI has, is a great help to distinguish more professional usage and therefore more urgent questions.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The platforms have similarities in terms of making the organization more data-driven. However, the use cases are different. SAP Analytics Cloud is used for reporting, deploying dashboards, and scheduling timely delivery of reporting and analytics. SAP Conversational AI is a more front-end product that end users can directly use to navigate the web application better. Both have strengths in their respective areas. Conversational AI is more recent and cutting edge.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The impact has been very positive for our business objectives. Through the chatbot, our clients can have an immediate response to any of their requests without the need of an intervention of a person or without the use of the telephone or email. Customer satisfaction has been much higher.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
