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Amazon Machine Learning vs TensorFlow: What are the differences?

Introduction

In this article, we will discuss the key differences between Amazon Machine Learning (AML) and TensorFlow. Both AML and TensorFlow are popular tools used in the field of machine learning, but they have some distinct characteristics that set them apart from each other.

  1. Ease of Use: Amazon Machine Learning is designed with simplicity and ease of use in mind. It provides a simplified interface that allows users to build machine learning models without requiring extensive coding knowledge. On the other hand, TensorFlow is a more advanced and versatile tool that offers greater flexibility and control over the machine learning process. It requires users to have a deeper understanding of machine learning concepts and programming skills.

  2. Scalability: When it comes to scalability, Amazon Machine Learning provides a built-in infrastructure that can handle large volumes of data and process them efficiently. It is well integrated with other Amazon Web Services (AWS) products, making it easy to scale up the machine learning models as needed. Conversely, TensorFlow allows users to deploy their models on a variety of hardware platforms, including CPUs, GPUs, and even distributed systems. This makes TensorFlow more suitable for large-scale and high-performance computing tasks.

  3. Pre-built Algorithms vs. Customizable Models: With Amazon Machine Learning, users can take advantage of pre-built algorithms for common machine learning tasks, such as binary classification, multiclass classification, and regression. These algorithms are optimized and can be easily applied to different datasets. In contrast, TensorFlow offers a wide range of customizable models, allowing users to build and train models from scratch or modify existing models for specific tasks. This flexibility comes at the cost of additional complexity in model development.

  4. Autonomous vs. Development-Driven: Amazon Machine Learning is designed to be an autonomous service that automates several steps of the machine learning pipeline, such as data preprocessing, model training, and deployment. This makes it ideal for users who want to quickly build and deploy machine learning models without much manual intervention. TensorFlow, on the other hand, gives users full control over the machine learning process and requires more development-driven steps, such as defining the architecture of the neural network, selecting and fine-tuning hyperparameters, and writing code for training and evaluation.

  5. Integration with Amazon Web Services: Amazon Machine Learning is tightly integrated with other Amazon Web Services, such as AWS S3 for data storage, AWS Lambda for serverless computing, and AWS Redshift for data warehousing. This makes it easier to build end-to-end machine learning pipelines using a unified infrastructure. While TensorFlow can also be integrated with various AWS services, it offers more flexibility in terms of deployment options, allowing users to deploy models on different platforms and technologies.

  6. Community and Ecosystem: TensorFlow has a large and active community of developers and researchers, which has contributed to its vast ecosystem of pre-trained models, libraries, and tools. This makes it easier for users to access and leverage existing resources for their machine learning projects. While Amazon Machine Learning also has a community, it is relatively smaller compared to TensorFlow, and the availability of pre-trained models and additional resources may be somewhat limited.

In summary, Amazon Machine Learning is a user-friendly, scalable, and autonomous service that offers simplicity and convenience for building machine learning models. On the other hand, TensorFlow provides more flexibility, control, and customization options for advanced machine learning tasks, but requires a higher level of expertise and development effort.

Advice on Amazon Machine Learning and TensorFlow

Hello everyone,

I am currently on an internship, and I am a new intern in an SME. My first mission is to choose the right tool for predictive sales analysis (management of the quantity in stock). I found several tools (paying and open source), and the company leaves the choice of tools to me (even paying). They suggest SAP Analytics Cloud as a first attempt (since we want a tool on the cloud too). I would like to have your proposals since I'm new to the business.

PS: I code in Python !! thank you in advance.

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Replies (3)
Nutchanon Ninyawee

In sales analysis, you might need some sort of timeseries prediction. I would recommend the sagemaker DeepAR. where you could co-op the seasonal effect into the model.

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philippe thiran
Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science · | 3 upvotes · 4.5K views

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

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PythonPythonTensorFlowTensorFlow

If you want to code it yourself and based on your desired output. RNN (Recurrent neural network) would be the right choice. You can code it using Tensorflow and use LSTM as the layers.

If you prefer on cloud with tools ready to use and not much coding, Amazon DeepAR Forecasting looks sufficient.

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Pros of Amazon Machine Learning
Pros of TensorFlow
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    • 32
      High Performance
    • 19
      Connect Research and Production
    • 16
      Deep Flexibility
    • 12
      Auto-Differentiation
    • 11
      True Portability
    • 6
      Easy to use
    • 5
      High level abstraction
    • 5
      Powerful

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    Cons of Amazon Machine Learning
    Cons of TensorFlow
      Be the first to leave a con
      • 9
        Hard
      • 6
        Hard to debug
      • 2
        Documentation not very helpful

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is 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.

      What is TensorFlow?

      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.

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