Skyvia vs. TensorFlow

Skyvia is easy to use and set up automated backups. It was one of several tools that we vetted for this purpose – and had the most favorable pricing and integrations to suit our needs. We wanted to have multiple CRM instances backed up daily and allow non-technical team members to manage this, as well as restore them in times of need. Skyvia has allowed us to do this with ease.

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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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• Skyvia is good at scheduled bidirectional data integration to/from multiple sources
• The mapping is done automatically which saves a lot of time
• I like that all connectors are included in the price, so I don't have to pay for using some new ones additionally

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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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• If there's something they can improve could be cheaper prices for LaTam
• They can market their services in a larger scale, globally.
• They can market their services in a larger scale, globally.

• 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.

Reliable, very simple to use, and a range of subscription tiers to suit different needs.

Cons:

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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

Loved using Skyvia for our projects, it saved time, allowed us to reach the desired outcome quickly and was easy to maintain. When we first used it we weren't sure we would be able achieve the outcome desired but during the trial it was quickly obvious we would be able to achieve what we needed for data connection and reporting.

Support of multiple components and ease of development.

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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

I think guys have a really good support team, and even from different time zones we managed to get worthy support.

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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Simple to setup and maintain

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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

Use of cloud for better execution power is recommended.

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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

Compared to similar user-friendly data integration systems like Integrate.io, Stitch, or Azure, Skyvia still wins in the number of connectors and pricing policy. Skyvia is a tool for companies like ours, starving for simple but, at the same time, robust and cost-effective data transfer solutions. It doesn't require additional knowledge for implementation and usage because it's no code. We'd recommend Skyvia as the best solution for non-technical staff.

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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

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• The core of our business lies in analyzing data from various sources, and while ETL itself is not our primary focus, it is a necessary process to achieve our goals. Thanks to Skyvia, we are able to streamline the ETL process at a reasonable price, allowing us to dedicate our efforts to data analysis without the burden of ETL complexities.

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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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Independently Invited by TrustRadius

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