What users are saying about
29 Ratings
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Score 8.3 out of 100
34 Ratings
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Score 7.3 out of 100

Likelihood to Recommend

Databricks Lakehouse Platform

Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.
Anonymous | TrustRadius Reviewer

TensorFlow

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).
Anonymous | TrustRadius Reviewer

Pros

Databricks Lakehouse Platform

  • It supports all data science programming languages like Python and R
  • it takes very few minutes to deploy models into production
  • it has tools that ensures collaborations between developers
Kofi Joshua | TrustRadius Reviewer

TensorFlow

  • 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.
Nitin Pasumarthy | TrustRadius Reviewer

Cons

Databricks Lakehouse Platform

  • Better Localized Testing
  • When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
  • Graphing Support went non-existent; when it was one of their compelling general engine.
Anonymous | TrustRadius Reviewer

TensorFlow

  • 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.
Nisha murthy | TrustRadius Reviewer

Usability

Databricks Lakehouse Platform

Databricks Lakehouse Platform 9.0
Based on 1 answer
This has been very useful in my organization for shared notebooks, integrated data pipeline automation and data sources integrations. Integration with AWS is seamless. Non tech users can easily learn how to use Databricks. You can have your company LDAP connect to it for login based access controls to some extent
Anonymous | TrustRadius Reviewer

TensorFlow

TensorFlow 9.0
Based on 1 answer
Support of multiple components and ease of development.
Anupam Mittal | TrustRadius Reviewer

Support Rating

Databricks Lakehouse Platform

No score
No answers yet
No answers on this topic

TensorFlow

TensorFlow 9.1
Based on 3 answers
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.
Anonymous | TrustRadius Reviewer

Implementation Rating

Databricks Lakehouse Platform

No score
No answers yet
No answers on this topic

TensorFlow

TensorFlow 8.0
Based on 1 answer
Use of cloud for better execution power is recommended.
Anupam Mittal | TrustRadius Reviewer

Alternatives Considered

Databricks Lakehouse Platform

I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.
Ann Le | TrustRadius Reviewer

TensorFlow

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
Anonymous | TrustRadius Reviewer

Return on Investment

Databricks Lakehouse Platform

  • Rapid growth of analytics within our company.
  • Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
  • Platform is easy to learn and Databricks provides excellent support and training.
  • Platform does not require a large DevOPs investment
Anonymous | TrustRadius Reviewer

TensorFlow

  • 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.
Shambhavi Jha | TrustRadius Reviewer

Pricing Details

Databricks Lakehouse Platform

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Databricks Lakehouse Platform Editions & Modules

Edition
Standard$0.071
Premium$0.101
Enterprise$0.131
  1. Per DBU
Additional Pricing Details

TensorFlow

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

TensorFlow Editions & Modules

Additional Pricing Details

Add comparison