LMS365 vs. TensorFlow

If a customer does not have SharePoint the entry in that kind of solution is a bit harder as the use of SharePoint can be so broad. It does not mean it is not the right solution as a company can use SharePoint LMS to start with and then expand to other functions and features. I strongly recommend to use SharePoint Standard or Enterprise and forgo the "free" SharePoint version as a) SharePoint's core functions and features are greatly enhanced in these two premium versions and b) more and more SharePoint LMS functions make use of these core Standard and Enterprise features in the future, like taxonomy, user profiles, etc. In general with all project it is as well recommended to have the full buy in by upper management and that the project initiative is fully supported. Adding such a solution (SharePoint LMS or any other LMS solution) will require the team not only to have a good plan on how the requirements can be achieved from a technical point of view, but how a training program can be rolled out to an organization of 5, 500, 5.000 or 50.000. The technical deployment of SharePoint LMS is measurable (I would say between 1-4 weeks based on the complexity, scale of the environment). 1-2 weeks of training (depending on the base knowledge of SharePoint in the company and the need to add knowledge of SharePoint LMS). That's it. Technically you are ready. If needed, any custom work, integration and development work comes on top. Where customer struggle is the availability and dedication of their own teams. Course content needs to be created, how should a course look like, what are the parameters, what are top ten things needed moving a course which was taught in a class room, but now to be delivered online. Buying the licenses is one thing, getting the solution up and running form a technical point of view is another, making it YOURS is the challenge!

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).

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• Creating an easy to navigate Course Catalog
• Enabling the training team to assign training easily and send out email reminders.
• Create sample training in the Sandbox to test thoughts and ideas.

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• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

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• Clarify the process for enrollment into courses
• When the user clicks into a different portion of the file library and then needs to return to a previous class the software forces you to go all the way back to the beginning of the coursework, this isn’t that painful just annoying.

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• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• 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.

It is very simple for me: As I said, I am (we are) selling and consulting around SharePoint LMS. SharePoint LMS is a killer application which needs to be in every company which has a vision and mission to deliver and create knowledge. So you can say Thomas (me) is biased, but I only encourage you to check out the solution to hear what we have to say and stack our solution against the other solutions out there.

Ultimately, in my opinion LMS365 is a bit clunky to use. It has most of the features you need, but most need to be configured by your technology department, e.g., SSO, user groups in Entra ID, notifications through Slack, teams, etc. If you're looking for an all-in-one solution, look elsewhere, as lms365 has several catches to its proposition.

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Support of multiple components and ease of development.

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The few times we actually needed support generally were during major upgrades of the system and getting a quick handle on how the configuration changed were the primary reasons.

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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.

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Use of cloud for better execution power is recommended.

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I will pick EdApp or Totara if you're after an all-in-one LMS solution that is feature-rich. For a university educational setting, Moodle continues to make sense. Safety Culture now owns EdApp, so over time, these platforms will likely be merged. LMS needs a huge overhaul to catch up.

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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

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• It will reduce the time
• We can speed up the work
• There are more advantages in any part of the organisation

• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

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