Mixpanel vs. TensorFlow

As a worker in the sales area, I see closely how complex it can be to evaluate the commercial funnel and Mixpanel has been an indispensable guide to prioritize above all what customers expect to receive from our company, and thus be able to determine the main service we offer. Without a doubt, Mixpanel has special functions to be the one that guides the route and marks the objectives much more clearly.

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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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• Mixpanel is a daily use application for everyone in my organization; it helps us have a better flow of information and interaction between work teams.
• The user interface of this platform is simple and has a wide variety of functions and resources to help us work in the most organized way, have better team coordination, and keep efficiency high.
• I love that it is so easy to program our calendar to our liking, so we can prioritize our activities and know what is pending, and the best thing is that I can update the calendar if necessary.
• The chat function is great to improve the interaction between colleagues and share work schedules and any information with third parties.

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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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• Mixpanel requires an explicit setting of events from your app. This means you need to be very thoughtful in the design of your events because missing one means you aren't collecting any data from it. Inserting it into the process later on then brings challenges in tracking when certain events came online.
• A tool like Mixpanel comes packed with features that sometimes are harder to discover. It's very easy to get sucked into one part of its toolset and not be aware of other tools which may be very useful.

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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.

It's not an all encompassing solution like Google Analytics tries to be, but MixPanel offers much easier to use and understandable data insights. That's valuable when juggling many responsibilities as startup life demands, so a renewal would be easily justified.

Relativity easy to use. Once you get the hang of it, very easy to create dashboards for different use cases. I split my dashboards between customers or use cases

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Support of multiple components and ease of development.

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We have only ever had to use their support once, when we were setting up the account, but their responses were prompt and the solutions were well documented. The people who solved our issues were helpful, even to non-tech people.

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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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Mixpanel has a great resource about their product, with videos on how to use it and real world examples from other companies on how they integrate Mixpanel into their business processes.

Again, somewhat annoying to be charged based on data points when many other analytics providers have one flat fee. Implementation was good, but I might have tracked a few more detailed points if I had the option.

Use of cloud for better execution power is recommended.

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• There was not really any other competitors for this specific use case that had a chance
• We used Google Analytics for some things but obviously, GA is anonymous data so not useful for product teams with users
• Other solutions out there seemed to be too expensive and this was the most recommended
• Heap seemed to be the best OTHER option but didn't take the time to evaluate

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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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• We've been able to increase the funnel conversions of one of our new product funnels from a 1% conversion rate to a 5% conversion rate.
• We've been able to increase the CTR on another of our main product pages from ~3% to ~10% (so far)
• We've been able to segment out how users from different traffic sources behave, allowing us to eliminate thousands of dollars of wasteful spending on advertising campaigns that weren't working.

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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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