Google Universal Analytics was an enterprise-level analytics solution that was sunset in July of 2024.
$150,000
Up to 1 Billion hits/month
TensorFlow
Score7.6 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
As I have discussed previously their insights were very useful. The second thing is since it is a Google product you will connect the data very easily from other platforms like Bigquery, Google Drive, etc. and even you can connect Google marketing platform. through this tool, you can track your live campaign how they were performing, and how it will be engaging your customer as well.
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).
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
It is an excellent cloud analytics platform that is easy to install and configure and easy to deploy and use, allowing us to measure web traffic and other tools.
It is an entirely online tool; it does not take up hard disk space like other desktop tools.
Since this tool is draggable, Google is constantly adding more features.
Even beginners who do not have a custom dashboard can get information. If there is a problem somewhere on the site that needs to be investigated, Google Analytics 360 will notify you.
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
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
Generally I think there is a lot you can do within the tool, but as it is a Google product it means there is limited support - something which I think lets all of the platform stacks down
There could be more visual signifiers to identify if a feature is a normal or 360 feature. This would mean you can really get to grips with what the extra more advanced elements are
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.
Google Analytics 360 is an upgraded version of the most widely used web/app analytics tracking tools in the market. The price is stable and predictable making it a long-term product of choice. It's easy to use and pairs so well with other Google Marketing Platform products.
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
The UI is very easy to navigate and use. The features are well designed and intuitive. As long as the user has a good understanding of basic digital analytics definitions and capabilities, this tool should be quite easy to use. I consider Google Analytics Premium to be the easiest of all of the enterprise solutions out there to use.
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
If you purchase Premium through a reseller like LunaMetrics, you are going to be taken care of. The additional amount of support and services that a reseller provides to make sure you have the best experience with the product is the reason why the reseller program exists to begin with. Support doesn't have to be just reactive, it can be proactive as well.
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.
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
There is a ton of information online about Google Analytics, but Google Analytics Premium users will have dedicated support and training from Google or an Authorized Reseller.
If you already have the basic version of GA installed, "getting" GA Premium happens immediately through a virtual flipping of the switch - no need to re-implement. You'll want to expand your use of custom dimensions and metrics (you get 10x the amount with Premium). Ideally, you'll be using a tag management solution to talk with GA Premium, in concert with implementing a dataLayer (to note, Google's Tag Manager platform is covered under the same GA Premium SLA, and it's free). There are some welcomed "configurations" with GA Premium, such as integrating with DoubleClick products, activating data driven attribution models, and building roll-up executive reports - but all of these are easy point and click solutions. In comparison with any other enterprise analytics solution, implementing GA and GA Premium is traditionally easier and more flexible. And if you have any trouble or need an extra set of hands for implementation, GA Certified Partners like LunaMetrics can help
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
Unless you have very complex and edge case analytics needs, Google Analytics [360 (formerly Google Analytics Premium)] is likely going to be the best choice. From both a cost and usability stand point, Google wins. Adobe has the edge case when you need to create really custom reports, dimensions, metrics, etc. In my experience, this is rarely the case and you end up biting off more than you can chew. Stick with Google unless you are or plan on hiring an Adobe Analytics expert.
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
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
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
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
