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Managed MLflow VS Open Data Hub

Compare Managed MLflow VS Open Data Hub and see what are their differences

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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.

Open Data Hub logo Open Data Hub

OpenDataHub
  • Managed MLflow Landing page
    Landing page //
    2023-05-15
  • Open Data Hub Landing page
    Landing page //
    2023-06-01

Managed MLflow features and specs

  • Scalability
    Managed MLflow leverages Databricks' cloud infrastructure, allowing for seamless scaling without worrying about underlying hardware limitations.
  • Ease of Use
    The integration with Databricks provides a user-friendly interface that simplifies the process of tracking and managing machine learning models.
  • Integration
    It natively integrates with other Databricks features and tools, enhancing workflows and improving collaboration between data scientists and engineers.
  • Security
    Managed MLflow benefits from Databricks' secure environment, which includes encryption, compliance standards, and access control measures.
  • Automation
    It offers features that automate various parts of the machine learning lifecycle, such as model training and deployment, reducing manual workload.
  • Support
    As a commercial solution, Managed MLflow provides professional support and services, ensuring reliable assistance and troubleshooting.

Possible disadvantages of Managed MLflow

  • Cost
    The managed service comes with a cost, which might be significant for small teams or startups when compared to an open-source setup.
  • Vendor Lock-in
    Using a managed service ties your workflows to the Databricks ecosystem, which can complicate migrations or integrations with other platforms.
  • Customization Limitations
    While Managed MLflow provides a streamlined user experience, it might limit flexibility on customization or specific feature requirements.
  • Dependency on Internet Connectivity
    As a cloud-based service, continuous, stable internet connectivity is required, which could be a downside for certain use cases.
  • Learning Curve
    Teams unfamiliar with the Databricks environment might face a learning curve to effectively utilize all features of Managed MLflow.

Open Data Hub features and specs

No features have been listed yet.

Managed MLflow videos

No Managed MLflow videos yet. You could help us improve this page by suggesting one.

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Open Data Hub videos

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Open Data Hub Introduction

More videos:

  • Review - Fraud Detection Using Open Data Hub on Openshift
  • Review - Installing Open Data Hub on OpenShift 4.1

Category Popularity

0-100% (relative to Managed MLflow and Open Data Hub)

Managed MLflow

Open Data Hub

Data Science And Machine Learning
78 78%
Data Science And Machine Learning
22% 22
Machine Learning
100 100%
Machine Learning
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Data Science Notebooks
100 100%
Data Science Notebooks
0% 0

User comments

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Social recommendations and mentions

Based on our record, Open Data Hub 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.

Open Data Hub mentions (3)

  • job scheduling for scientific computing on k8s?
    Perhaps have a look at OpenDataHub. While geared for Openshift, see if they solved some of your concerns. Source: about 2 years ago
  • Elyra 2.2: R support, updated CLI, and more
    A common approach is to deploy JupyterHub on Kubernetes and configure it for Elyra, like it is done in Open Data Hub on the Red Hat OpenShift Container platform. - Source: dev.to / over 4 years ago
  • Automate your machine learning workflow tasks using Elyra and Apache Airflow
    If you are interested in running pipelines on Apache Airflow on the Red Hat OpenShift Container Platform, take a look at Open Data Hub. Open Data Hub is an open source project (just like Elyra) that should include everything you need to start running machine learning workloads in a Kubernetes environment. - Source: dev.to / over 4 years ago

What are some alternatives?

When comparing Managed MLflow and Open Data Hub, 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.

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

5Analytics - The 5Analytics AI platform enables you to use artificial intelligence to automate important commercial decisions and implement digital business models.

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

Spell - Deep Learning and AI accessible to everyone

C3 AI Suite - The C3 AI Suite uses a model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise-scale AI applications.

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