AWS Personalize
Real-time personalization and recommendation engine in AWS subtitle
- Open Source
AWS Personalize Alternatives
The best AWS Personalize alternatives based on verified products, community votes, reviews and other factors.
Latest update:
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/scikit-learn-alternatives
scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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/machine-learning-in-python-alternatives
Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.
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Get proxy servers featuring IPv4, HTTP/HTTPs, and SOCKS4/5 protocols. Choose from static and rotating IP addresses. ProxyCompass is here to support your business around the clock.
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/google-cloud-tpu-alternatives
Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.
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/amazon-forecast-alternatives
Accurate time-series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required.
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/python-recsys-alternatives
python-recsys is a python library for implementing a recommender system.
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/bigml-alternatives
BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.
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/golearn-alternatives
GoLearn is a machine learning library for Go that implements the scikit-learn interface of Fit/Predict.
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/qubole-alternatives
Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
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/microsoft-recommendations-api-alternatives
Obtains details of a cached recommendation.
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/weka-alternatives
WEKA is a set of powerful data mining tools that run on Java.
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/google-cloud-automl-alternatives
Train custom ML models with minimum effort and expertise
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/google-recommender-api-alternatives
Google Recommender API is a service on Google Cloud that provides usage recommendations for Google Cloud resources.
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/aws-sagemaker-ground-truth-alternatives
Build highly accurate training datasets using machine learning and reduce data labeling costs by up to 70%.