Jive Software, part of the Aurea family of customer experience solutions, provides the gateway to an organization's most important assets – its knowledge and people. Jive's interactive intranet solution promises to connect people, information and ideas to help businesses outpace their competitors. The vendor says the product has more than 30 million users worldwide across every industry, and is consistently recognized as a leader by top analyst firms.
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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.
It's definitely worth considering Jive for the type of application we've developed i.e. a central shared repository for all employees to host and discuss information. I can't say I have ever used a superior tool, but they may exist. I'm just not sure I would want to use it exclusively for file hosting, though. It does integrate with various other tools, so perhaps it would be fine if used in conjunction with another tool for that purpose.
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. TR verified that a representative sample of customers was invited. 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
Presence of Russian language (localization can be independently established)
You can configure several information tapes with different themes. One for work, the second for communication
A newly-arrived network user immediately receives a prepared block for beginners. After completing several game tasks, the user will receive the basics of using Jive.
There is a template for each scenario. There is even a template for planning R&D, and there are more than twenty of them.
Integrates with MS Office, Google Drive, Google Docs
There are all platforms (even Winphone and blackberry)
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
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.
There are always external factors that can impact this decision but currently, the Jive platform is maintaining its lead in the market place in this area. If the innovation in this space by Jive continues, then this number will remain high. Integration with other systems and adaptability to changes in the market or in client needs will also make this decision hard to predict more then 6 months into the future
It was harder to use that expected. The admin needs to be code savvy to truly customize the system. And users need to trained on the system and the setup. Trainings and monitoring need to continue to enforce use.
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
Uptime was OK. But there was one day that the system crashed for a whole day. Our company was unable to operate. And all the plugins to word/excel froze causing those systems to freeze.
Jive posted a statement to the media saying all customers were up, but we were not.
They did an OK job when I needed them. Except for the one day the system went down. Jive pointed the finger at the hosting company, and the hosting company pointed the finger at Jive. No reliable information came to us.
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
Jive online training is there. It is OK/average. I feel some other companies are doing better. It is not a piece that is required to have a successfully implementation, but it could be useful to improve it
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
We had a Google phone number set up before and Jive was a lot better option than that. It is more consistent and can be configured much easier and with more advanced settings. Additionally, based on the pricing as well as working with the rep on our account, it was a perfect option for us
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
reduce amount of files/documents scattered & lost across shared drives
increased discovery, awareness and interaction of historically more separated individuals & team functions across the organisation
from an IT perspective, we've benefited from improved IT operations (e.g. troubleshooting info shared and easily searched/found with all team members - such that even junior team members can solve technical problems outside of business hours, lessening the burden for standby/call-in for more senior team members)
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
