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State.of.dev VS Managed MLflow

Compare State.of.dev VS Managed MLflow and see what are their differences

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Contents:
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State.of.dev logo State.of.dev

Visualizing the current state of development

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.
  • State.of.dev Landing page
    Landing page //
    2022-05-06
  • Managed MLflow Landing page
    Landing page //
    2023-05-15

State.of.dev features and specs

No features have been listed yet.

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.

Category Popularity

0-100% (relative to State.of.dev and Managed MLflow)

State.of.dev

Managed MLflow

Productivity
100 100%
Productivity
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Business & Commerce
100 100%
Business & Commerce
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
100% 100

User comments

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What are some alternatives?

When comparing State.of.dev and Managed MLflow, you can also consider the following products

Algorithm Visualizer - Write down your algorithm to be visualized

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 Algorithm Changes - Shows fluctuations in SERPs matched with algorithmic updates

Weights & Biases - Developer tools for deep learning research

Pretty Funnels - Pretty Funnels is an marketing tool designed to organize and optimize your digital funnels, with a strong emphasis on simplicity and effectiveness.

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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