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Google Cloud Dataproc VS Managed MLflow

Compare Google Cloud Dataproc VS Managed MLflow and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Google Cloud Dataproc logo Google Cloud Dataproc

Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

Managed MLflow logo Managed MLflow

Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • Managed MLflow Landing page
    Landing page //
    2023-05-15

Google Cloud Dataproc videos

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Dataproc

Managed MLflow videos

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Category Popularity

0-100% (relative to Google Cloud Dataproc and Managed MLflow)

Google Cloud Dataproc

Managed MLflow

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Big Data
100 100%
Big Data
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
100% 100

User comments

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Social recommendations and mentions

Based on our record, Google Cloud Dataproc seems to be more popular. It has been mentiond 3 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.

Google Cloud Dataproc mentions (3)

  • Connecting IPython notebook to spark master running in different machines
    I have also a spark cluster created with google cloud dataproc. Source: about 1 year ago
  • Why we don’t use Spark
    Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / about 2 years ago
  • Data processing issue
    With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: over 2 years ago

Managed MLflow mentions (0)

We have not tracked any mentions of Managed MLflow yet. Tracking of Managed MLflow recommendations started around Mar 2021.

What are some alternatives?

When comparing Google Cloud Dataproc and Managed MLflow, you can also consider the following products

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Weights & Biases - Developer tools for deep learning research

HortonWorks Data Platform - The Hortonworks Data Platform is a 100% open source distribution of Apache Hadoop that is truly...

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

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