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cinnaroll.ai VS Managed MLflow

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

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cinnaroll.ai logo cinnaroll.ai

Rapid & simple machine learning model deployment. Without Ops overhead. Unified, streamlined way of testing, experimenting, reviewing, selecting, deploying, monitoring and retraining ML models.

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.
  • cinnaroll.ai Landing page
    Landing page //
    2023-05-24
  • Managed MLflow Landing page
    Landing page //
    2023-05-15

Category Popularity

0-100% (relative to cinnaroll.ai and Managed MLflow)

cinnaroll.ai

Managed MLflow

Data Science And Machine Learning
13 13%
Data Science And Machine Learning
87% 87
Machine Learning
21 21%
Machine Learning
79% 79
Data Science Notebooks
0 0%
Data Science Notebooks
100% 100
Machine Learning Tools
16 16%
Machine Learning Tools
84% 84

User comments

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What are some alternatives?

When comparing cinnaroll.ai and Managed MLflow, you can also consider the following products

Iguazio - Iguazio is a platform that allows users to bring their data science to life, and it automates the MLOps with end-to-end machine learning pipelines while transforming AI projects into the real world.

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.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Weights & Biases - Developer tools for deep learning research

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

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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