Alternatives to Amazon Personalize logo

Alternatives to Amazon Personalize

TensorFlow, scikit-learn, Keras, PyTorch, and ML Kit are the most popular alternatives and competitors to Amazon Personalize.
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What is Amazon Personalize and what are its top alternatives?

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.
Amazon Personalize is a tool in the Machine Learning as a Service category of a tech stack.

Amazon Personalize alternatives & related posts

related TensorFlow posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 596.2K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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StackShare Editors
StackShare Editors
| 4 upvotes 99.9K views
atUber TechnologiesUber Technologies
Cassandra
Cassandra
Apache Spark
Apache Spark
TensorFlow
TensorFlow

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

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scikit-learn logo

scikit-learn

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Easy-to-use and general-purpose machine learning in Python
scikit-learn logo
scikit-learn
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Amazon Personalize logo
Amazon Personalize
Keras logo

Keras

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Deep Learning library for Theano and TensorFlow
Keras logo
Keras
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Amazon Personalize logo
Amazon Personalize

related Keras posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 596.2K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more
PyTorch logo

PyTorch

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A deep learning framework that puts Python first
PyTorch logo
PyTorch
VS
Amazon Personalize logo
Amazon Personalize

related PyTorch posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 596.2K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more
ML Kit logo

ML Kit

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Machine learning for mobile developers (by Google)
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    ML Kit logo
    ML Kit
    VS
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    Amazon Personalize
    Azure Machine Learning logo

    Azure Machine Learning

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    A fully-managed cloud service for predictive analytics
      Be the first to leave a pro
      Azure Machine Learning logo
      Azure Machine Learning
      VS
      Amazon Personalize logo
      Amazon Personalize
      CUDA logo

      CUDA

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      It provides everything you need to develop GPU-accelerated applications
        Be the first to leave a pro
        CUDA logo
        CUDA
        VS
        Amazon Personalize logo
        Amazon Personalize
        Amazon Machine Learning logo

        Amazon Machine Learning

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        Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn...
          Be the first to leave a pro
          Amazon Machine Learning logo
          Amazon Machine Learning
          VS
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          Amazon Personalize

          related Amazon Machine Learning posts

          Julien DeFrance
          Julien DeFrance
          Principal Software Engineer at Tophatter | 2 upvotes 24.7K views
          atSmartZipSmartZip
          Serverless
          Serverless
          AWS Lambda
          AWS Lambda
          Amazon Machine Learning
          Amazon Machine Learning
          Amazon SageMaker
          Amazon SageMaker
          #PaaS
          #GCP
          #FaaS

          Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

          Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

          Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

          Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

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