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Google Maps vs TensorFlow: What are the differences?
What is Google Maps? Build highly customisable maps with your own content and imagery. Create rich applications and stunning visualisations of your data, leveraging the comprehensiveness, accuracy, and usability of Google Maps and a modern web platform that scales as you grow.
What is TensorFlow? Open Source Software Library for Machine Intelligence. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Google Maps belongs to "Mapping APIs" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".
"Free" is the top reason why over 239 developers like Google Maps, while over 15 developers mention "High Performance" as the leading cause for choosing TensorFlow.
Lyft, PedidosYa, and Movielala are some of the popular companies that use Google Maps, whereas TensorFlow is used by Uber Technologies, 9GAG, and VSCO. Google Maps has a broader approval, being mentioned in 1964 company stacks & 1074 developers stacks; compared to TensorFlow, which is listed in 195 company stacks and 126 developer stacks.
From a StackShare Community member: "We're a team of two starting to write a mobile app. The app will heavily rely on maps and this is where my partner and I are not seeing eye-to-eye. I would like to go with an open source solution like OpenStreetMap that is used by Apple & Foursquare. He would like to go with Google Maps since more apps use it and has better support (according to him). Mapbox is also an option but I don’t know much about it."
I use Mapbox because We need 3D maps and navigation, it has a great plugin for React and React Native which we use. Also the Mapbox Geocoder is great.
Google Maps is best because it is practically free (they give you $300 in free credits per month and it's really hard to go over the free tier unless you really mean business) and it's the best!
I use OpenStreetMap because that has a strong community. It takes some time to catch up with Google Maps, but OpenStreetMap will become great solution.
I use Google Maps because it has a lot of great features such as Google's rich APIs, geolocation functions, navigation search feature, street map view, auto-generated 3D city map.
I use OpenStreetMap because i have the control of the environment, using Docker containers or bare-metal servers.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
Google Maps will be used as an external API in order to mark locations on a map for display in the UI and it is an extensive and well-known framework. Tensorflow will be used for Machine Learning as it is open-source, customizable to different types of machine learning algorithms and lets you serve your model with a REST API. Tensorflow also has a lot of support and documentation which makes it easier for to start with it. Tensorflow is also written with Python. Python is easy to write in, efficient and commonly used in ML applications. In relation to Python, SnipsNLU (not shown on stackshare) will also be used in order to easily train NLU models. PredictHQ (not shown on stackshare) will be used for event data and has an easily accessible API. Twitter API will also be used in order to collect social media data and there are many endpoints it currently offers to query tweets in various ways (stackshare doesn't show this utility in the stack yet)
Pros of Google Maps
- Free253
- Address input through maps api136
- Sharable Directions81
- Google Earth47
- Unique46
- Custom maps designing3
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of Google Maps
- Google Attributions and logo4
- Only map allowed alongside google place autocomplete1
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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