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Microsoft Recommendations API VS Google CLOUD AUTOML

Compare Microsoft Recommendations API VS Google CLOUD AUTOML and see what are their differences

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Microsoft Recommendations API logo Microsoft Recommendations API

Obtains details of a cached recommendation.

Google CLOUD AUTOML logo Google CLOUD AUTOML

Train custom ML models with minimum effort and expertise
  • Microsoft Recommendations API Landing page
    Landing page //
    2023-02-12
  • Google CLOUD AUTOML Landing page
    Landing page //
    2023-07-30

Category Popularity

0-100% (relative to Microsoft Recommendations API and Google CLOUD AUTOML)

Microsoft Recommendations API

Google CLOUD AUTOML

Data Science Tools
100 100%
Data Science Tools
0% 0
Data Science And Machine Learning
19 19%
Data Science And Machine Learning
81% 81
AI
0 0%
AI
100% 100
Technical Computing
25 25%
Technical Computing
75% 75

User comments

Share your experience with using Microsoft Recommendations API and Google CLOUD AUTOML. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Google CLOUD AUTOML seems to be more popular. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Microsoft Recommendations API mentions (0)

We have not tracked any mentions of Microsoft Recommendations API yet. Tracking of Microsoft Recommendations API recommendations started around Mar 2021.

Google CLOUD AUTOML mentions (6)

  • Is there going to be engines dedicated to creating AI?
    There are several no-code AI websites that you can use like Amazon SageMaker, Apple CreateML or Google AutoML. Source: about 1 year ago
  • How AWS and GCP Compare: The Top 5 Differences
    GCP, on the other hand, offers two top options: Google Cloud AutoML, for beginners, and Google Cloud Machine Learning Engine, for handling tasking projects. GCP also provides Tenserflow and Vertex AI complicated machine learning abilities. - Source: dev.to / over 1 year ago
  • Discussion Thread
    Just outsource the work to Google or Amazon. Source: over 2 years ago
  • Is GitHub Copilot a Threat to Developers? (Spoiler: It’s Not
    We can also note the appearance of Machine Learning, creating dynamic processes over data that would have been tedious to analyse, either by hand or through specific code. This enables writing potentially complex behaviours with a few lines of code in some cases. Even then, there is some automation of it to the point where you only have to provide data to get working results. - Source: dev.to / almost 3 years ago
  • Are there any ready-to-use image AI programs for dummies?
    You might want to check out automl Google AutoML. Source: almost 3 years ago
View more

What are some alternatives?

When comparing Microsoft Recommendations API and Google CLOUD AUTOML, you can also consider the following products

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

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

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

machine-learning in Python - 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.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

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

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