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Azure Machine Learning vs TensorFlow: What are the differences?

What is Azure Machine Learning? A fully-managed cloud service for predictive analytics . Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.

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.

Azure Machine Learning and TensorFlow are primarily classified as "Machine Learning as a Service" and "Machine Learning" tools respectively.

Uber Technologies , 9GAG , and VSCO are some of the popular companies that use TensorFlow, whereas Azure Machine Learning is used by Microsoft , Bluebeam Software , and Petra . TensorFlow has a broader approval, being mentioned in 195 company stacks & 126 developers stacks; compared to Azure Machine Learning, which is listed in 12 company stacks and 8 developer stacks.

Decisions about Azure Machine Learning and TensorFlow
Xi Huang
Developer at University of Toronto · | 8 upvotes · 80.1K views

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.

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Pros of Azure Machine Learning
Pros of TensorFlow
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  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
  • 2
    Is orange

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Cons of Azure Machine Learning
Cons of TensorFlow
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  • 9
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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What companies use Azure Machine Learning?
What companies use TensorFlow?
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What tools integrate with Azure Machine Learning?
What tools integrate with TensorFlow?

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What are some alternatives to Azure Machine Learning and TensorFlow?
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
Azure Databricks
Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
Amazon SageMaker
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
See all alternatives