Microsoft Machine Learning Server Alternatives & Competitors

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Key Scikit-learn features:

Ease of Use Extensive Documentation and Community Support Integration with Other Libraries Variety of Algorithms

Open Source

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.

Key machine-learning in Python features:

Ease of Use Rich Ecosystem Community Support Integration Capabilities

Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI.

Key Grapple features:

Automatic Data Refresh Universal Data Library Natural Language Map Data

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Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

Key Google Cloud TPU features:

High Performance Scalability Ease of Integration Cost-Effective

Open Source

Accurate time-series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required.

Key Amazon Forecast features:

Automated Machine Learning Integration with AWS Ecosystem Variety of Algorithms Scalability

Real-time personalization and recommendation engine in AWS.

Key AWS Personalize features:

Personalization Accuracy Easy Integration Scalability Real-time Recommendations

Open Source

python-recsys is a python library for implementing a recommender system.

Key python-recsys features:

Ease of Use Open Source Collaborative Filtering Integration

Open Source

GoLearn is a machine learning library for Go that implements the scikit-learn interface of Fit/Predict.

Key GoLearn features:

Ease of Use Integration with Go Active Community Basic Machine Learning Algorithms

Open Source

Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

Key Qubole features:

Scalability Multi-cloud Support Unified Interface Cost Management

Open Source

Crab is a Python framework for building recommender engines.

Key Crab features:

Ease of Use Flexibility Open Source Compatibility with Python

WEKA is a set of powerful data mining tools that run on Java.

Key WEKA features:

User-Friendly Interface Wide Range of Algorithms Open Source Extensive Documentation

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.

Key XGBoost features:

Efficiency Scalability Regularization Flexibility

Open Source

BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.

Key BigML features:

User-Friendly Interface Wide Range of Algorithms Ease of Integration Visualization Tools

Obtains details of a cached recommendation. .

Key Microsoft Recommendations API features:

Integration Personalization Scalability Real-time Recommendations