Cloudera Data Platform vs TensorFlow

In the creation of maintenance models Cloudera can excel greatly, because it allows you to create very scalable and applicable processes in different types of technology, either for reporting based purely on data, or to implement its analysis modalities to develop IT projects. In case of requiring detailed review of different phases of IT processes, Cloudera can be useful in security operations that require high expertise in shared data.

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 networks2. 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).

Support of multiple components and ease of development.

We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.

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.

Use of cloud for better execution power is recommended.