Neptune vs Streamlit

Overview

Streamlit

Stacks403

Followers407

Votes12

GitHub Stars42.1K

Forks3.9K

Neptune

Stacks16

Followers38

Votes2

Neptune vs Streamlit: What are the differences?

Introduction: In this comparison, we will examine the key differences between Neptune and Streamlit.

Feature Focus: Neptune primarily focuses on experiment tracking, versioning, and collaboration for machine learning projects, providing detailed tracking of experiments, metrics, and hyperparameters. In contrast, Streamlit is a framework used for easily creating web applications with Python, allowing users to build interactive data apps quickly.
User Interface: Neptune offers a web-based interface for users to track and manage ML experiments, with options to visualize metrics and results. On the other hand, Streamlit provides a simple API to build web apps that can be rendered in a browser, with a strong focus on ease of use and real-time updates.
Deployment Options: Neptune is typically used for tracking ML experiments and does not serve as a deployment platform for web applications. In contrast, Streamlit is specifically designed for deploying web apps, making it an effective tool for sharing interactive data visualizations and applications.
Customization Level: Neptune provides a standardized interface for experiment tracking, providing specific functionalities for ML projects. Streamlit, on the other hand, offers a high level of customization, allowing users to create unique and tailored web applications with Python code.
Community and Support: Neptune offers strong support for ML-related tasks and has a community focused on machine learning experimentation and collaboration. Streamlit also has an active community but is more diverse, catering to a broader range of web application development needs.
Pricing Structure: Neptune typically offers a tiered pricing structure based on the number of projects or users, while Streamlit is an open-source platform with no cost associated with its basic usage, making it more accessible for individuals and smaller teams.

In Summary, Neptune and Streamlit differ in their focus on feature set, user interface, deployment options, customization level, community support, and pricing structure.

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Detailed Comparison

Streamlit	Neptune
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.	It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.
Free and open source; Build apps in a dozen lines of Python with a simple API; No callbacks; No hidden state; Works with TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib, Seaborn, Altair, Plotly, Bokeh, Vega-Lite, and more	Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
GitHub Stars 42.1K	GitHub Stars -
GitHub Forks 3.9K	GitHub Forks -
Stacks 403	Stacks 16
Followers 407	Followers 38
Votes 12	Votes 2
Pros & Cons
Pros 11 Fast development 1 Fast development and apprenticeship	Pros 1 Supports both gremlin and openCypher query languages 1 Aws managed services Cons 1 Doesn't have proper clients for different lanuages 1 Doesn't have much community support 1 Doesn't have much support for openCypher clients
Integrations
Python Plotly.js PyTorch Pandas Bokeh Keras NumPy Matplotlib TensorFlow Altair GraphQL	PyTorch Keras R Language MLflow Matplotlib

What are some alternatives to Streamlit, Neptune?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

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Neptune vs Streamlit

Overview