Jitterbit vs. TensorFlow

This is a great tool for bringing data out of your locked, internal systems and getting it into the cloud. It meshes well with Salesforce and is fairly easy to use, helping the transition from other older, more complex tools into a more modern environment. It has lots of competition in this space and some are better than others, but if your data is straight forward and you know it well, Jitterbit will get the job done. If you are not as close or comfortable with your data and need to do some wildly complex migrations, there might be better packages out there for you.

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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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• UI is super easy to understand with a low learning curve so admins can figure it out and maintain it without breaking anything.
• It's FREE! There's a paid version too but I like that you can use most of the features for free and they're not pushy with buying.
• There's a great user community that you can google and ask any questions. Most problems I've encountered have been posted and answered already.

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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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• Migrating operations from QA to Production work well for initial deployment, however, when migrating an update to an existing job to production, sometimes certain project items are duplicated. This is not the end of the world... the duplicates can be removed, but would be nice if it was not required.
• I have not found a way to trap under-the-covers SOAP errors (for example, when a query you are running against Salesforce takes too long). You get a warning error in the operation log that the job only pulled a "partial" file, but it does not fail.

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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.

I have been evaluating other tools as a continuous improvement practice. I would like something that would be easier to use for a non-technical user. I work for a small organization and have no back-up for Jitterbit if something happens to me. We don't have the technically savvy employees to understand it.

Support of multiple components and ease of development.

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They have some of the best support of any software vendor that we use. They always get our issues resolved quickly

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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Evaluated Dell Boomi and Celigo as alternatives prior to purchasing Jitterbit. We went with Jitterbit at that time because we could handle all changes ourselves without any assistance from Jitterbit, and we liked their size and nimbleness. Dell Boomi was too big for us, and Celigo at that time did not have a self-service model. Every change had to go through them (although that has since changed). We were not in a position to be able to wait for someone to make changes for us given the rate of change within the business.

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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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• The time it takes to connect systems has reduced by orders of magnitude. Previously, we would custom-develop connectors between various systems and they would all be managed by different vendors. With Jitterbit speed-to-deploy and the efficiency gained by managing all connections in one dashboard has been the greatest piece of the ROI.

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• 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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