Software Alternatives & Reviews

Managed MLflow VS Iterative.ai

Compare Managed MLflow VS Iterative.ai and see what are their differences

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.

Iterative.ai logo Iterative.ai

Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
  • Managed MLflow Landing page
    Landing page //
    2023-05-15
  • Iterative.ai Landing page
    Landing page //
    2023-08-18

Managed MLflow videos

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Iterative.ai videos

Reimagining DevOps for ML by Elle O'Brien, Iterative.ai

Category Popularity

0-100% (relative to Managed MLflow and Iterative.ai)
Data Science And Machine Learning
Data Science Notebooks
54 54%
46% 46
Machine Learning Tools
47 47%
53% 53
Machine Learning
55 55%
45% 45

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Managed MLflow and Iterative.ai

Managed MLflow Reviews

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Iterative.ai Reviews

  1. Rare

    The people at iterative.ai are special.

    Its hard to describe quickly, but if you're a potential client or employee--you could easily go your entire career unaware that groups like this exist.

    Their tools (like DVC) are exceptional, but I write this review because one need only interact with the people there to understand why they're execptional.

    The culture there is one that can only exist when the founding talent is top-tier. The experience you'll have, though, is so much more than that.

    Recommend whole-heatedly.

    👍 Pros:    Constantly improving|Quality|Community

Social recommendations and mentions

Based on our record, Iterative.ai 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.

Managed MLflow mentions (0)

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

Iterative.ai mentions (6)

  • Work with Google Drive files locally
    PyDrive2 is am open-source python package maintained by the awesome people at Iterative. And it is very easy to install:. - Source: dev.to / about 1 year ago
  • Any MLOps platform you use?
    These three are made by Iterative.ai, and seem like very clean implementations of MLOps tooling - especially if you aren't dealing with massive data. https://iterative.ai/. Source: about 1 year ago
  • How does your data science team collaborate?
    For what it's worth. (Full disclosure: I'm the community manager at Iterative (DVC,et.al.) Just wanted to make you aware of our online course (free) that we created specifically for Data Scientists (https://learn.iterative.ai). We know that bridging the gap between prototype to production/ jupyter notebook to reproducible/software engineering compatible, is a challenge. That's why we created the course. To also... Source: over 1 year ago
  • Advice about Infra and IaC
    What do you think of iterative.ai tools like dvc or cml? I have no direct experience, but I am looking at setting up something similar to what you need for a personal project. Source: almost 2 years ago
  • TPI - Terraform provider for ML/AI & self-recovering spot-instances
    Hey all, we (at iterative.ai) are launching TPI - Terraform Provider Iterative https://github.com/iterative/terraform-provider-iterative. Source: about 2 years ago
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What are some alternatives?

When comparing Managed MLflow and Iterative.ai, 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.

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.

Weights & Biases - Developer tools for deep learning research

MCenter - Machine Learning Operationalization

Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

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