Software Alternatives, Accelerators & Startups
Register | Login

Count VS Managed MLflow

Compare Count VS Managed MLflow and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
Contents:
  1. » Base Details
  2. » Reviews
  3. » Alternatives

Count logo Count

Real-time Shake Shack line counter using machine learning

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.
  • Count Landing page
    Landing page //
    2020-02-05
  • Managed MLflow Landing page
    Landing page //
    2023-05-15

Category Popularity

0-100% (relative to Count and Managed MLflow)

Count

Managed MLflow

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
17 17%
Data Science And Machine Learning
83% 83
AI
100 100%
AI
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
100% 100

User comments

Share your experience with using Count and Managed MLflow. For example, how are they different and which one is better?
Log in or Post with

What are some alternatives?

When comparing Count and Managed MLflow, you can also consider the following products

Apple Machine Learning Journal - A blog written by Apple engineers

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.

ML Showcase - A curated collection of machine learning projects

Weights & Biases - Developer tools for deep learning research

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

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.

Loading...