JAMS vs. TensorFlow

There's probably better schedulers out there. JAMS is good for an on-premises/classic IT implementation. JAMS is well-suited for use cases such as collecting a file from a shared location and uploading it to an API, running scripts on servers, and handling middleware tasks that process files or data between handoffs across different locations. JAMS is best suited to environments
with IT staff who can develop, test, implement, maintain, and troubleshoot
scripts; it does not use natural language processing (so it requires in-depth
knowledge of the scripting language in use), and it does not appear to offer
native dashboarding or reporting that is easily accessible to all users.

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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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• JAMS works well to organize jobs into logical folders with unique permissions for each folder
• JAMS makes it easy to set up alerts for job failures and other notifications
• Setting up scheduling times with normal human wording, such as Weekdays, or 2nd Tuesday of the month, etc.

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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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• Would love to see JAMS become a SaaS type of product instead of needing to be hosted by ourselves
• having lots of jobs in the monitor area can lead to performance issues when running the client locally
• dashboard and reporting tools could be more user friendly and provide more functionality to help monitoring teams spot recurring issues easily

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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.

JAMS is a critical resource free up people to do other things and ensuring that processes and tasks are run consistently. We are also confident that procedures are run consistently and on time or as soon as the necessary data is available. With automated job failure notification, we are not required to check that jobs are running correctly.

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JAMS is very user friendly; you hardly need to do coding. The only thing that I would say a challenge is setting it up, but that's because you barely know the product yet and, in every processing, setting up is the difficult part. But once you've set it up and you are going to use it, you will really feel that it is worth to invest in this kind of software solution, it really does it job very well.

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Support of multiple components and ease of development.

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We didnt really encounter any downtime, no issues encountered during 2 years of use of JAMs also our client barely raise an issue with JAMS, mostly the issues is on the batch jobs that jams executes. So I would gave it a perfect 10, very reliable hardly encounters any error and bug

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JAMS performance is very great, there are no issues raised with the performance, it just like nothing happens on the job after integration it gives you this monitoring capability, no reports and bugs raised on the performance, we didnt do integration with other software only database and with use of JAMS agent to different servers

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I've never had to wait more than a day for a response to any email queries submitted. We had a very positive experience using support hours during out migration process from v6 to v7. We've also recently had a weeklong group training course where all attendees were positive about the learning outcomes, a shoutout to Jose who did both the migration and the weeklong course!

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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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People that were involved in the POC found the training a lot easier to follow. I think most people would have preferred to just get the training material and run through themselves.

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I Was not part of the original Implementation, and the persons did that are no longer with the Organization. But I was part of the recent Upgrade process a year ago and I am the JAMS admin and was very pleased

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Use of cloud for better execution power is recommended.

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JAMS is WAY more advanced, it isn't a fair comparison. The history is easy to get through. It is easy to get alerts of complete to failed and with a log. Adding jobs is extremely easy that even my teammates who do not manange the software are able to set them up. With the new web component we are very excited for the future of JAMS advancements.

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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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i didnt have any involvement in the sales process, however we purchased the product so it had to be decent.

The product is quite flexible. There are a number of features and functions that we use on a daily basis, and there are many features that are available that we have not yet needed or explored (like setting up jobs with the ability to do FTP or Sftp file transfers).

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i didnt have any involvement in the sales process, however we purchased the product so it had to be decent.

• Our company depends on the JAMS Scheduler in executing a large number of SAP Jobs. In never having had a true Enterprise Scheduler such as JAMS before now, we are very happy with the results.
• There a number of features in JAMS for setting up schedules and dependencies on other jobs. This helps our company achieve the necessary workflows for Job execution. This optimization saves on system resources and keeps Jobs flowing smoothly.
• We are very happy that JAMS is a robust solution with High Availability. This is necessary for Enterprise products, to reduce downtime, which we have not had as a result. This feature definitely saves our company money by reducing or eliminating unexpected downtime.

• 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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