nannyml VS deep-significance

Compare nannyml vs deep-significance and see what are their differences.

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nannyml deep-significance
7 6
1,746 315
1.8% -
8.8 4.0
6 days ago 6 months ago
Python Python
Apache License 2.0 GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

nannyml

Posts with mentions or reviews of nannyml. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning nannyml yet.
Tracking mentions began in Dec 2020.

deep-significance

Posts with mentions or reviews of deep-significance. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning deep-significance yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing nannyml and deep-significance you can also consider the following projects:

evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

cuttle-cli - Cuttle automates the transformation of your Python notebook into deployment-ready projects (API, ML pipeline, or just a Python script)

Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.

barfi - Python Flow Based Programming environment that provides a graphical programming environment.

ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models

eurybia - ⚓ Eurybia monitors model drift over time and securizes model deployment with data validation

cyclops - Toolkit for health AI implementation

deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]

openrec - OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms

pytest-visual - A visual testing framework for ML with automated change detection