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Managed MLflow VS Google Cloud Dataproc

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

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

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 logo Google Cloud Dataproc

Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
  • Managed MLflow Landing page
    Landing page //
    2023-05-15
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09

Managed MLflow videos

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Google Cloud Dataproc videos

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Dataproc

Category Popularity

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

Managed MLflow

Google Cloud Dataproc

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

Managed MLflow mentions (0)

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

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

What are some alternatives?

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

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.

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

Weights & Biases - Developer tools for deep learning research

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

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.

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

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