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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Datatron vs scikit-learn

Datatron vs scikit-learn

OverviewComparisonAlternatives

Overview

scikit-learn
scikit-learn
Stacks1.3K
Followers1.1K
Votes45
GitHub Stars63.9K
Forks26.4K
Datatron
Datatron
Stacks0
Followers10
Votes0

scikit-learn vs Datatron: What are the differences?

scikit-learn: Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license; Datatron: Production AI Model Management at Scale. Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.

scikit-learn and Datatron belong to "Machine Learning Tools" category of the tech stack.

scikit-learn is an open source tool with 39.2K GitHub stars and 19.2K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

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Detailed Comparison

scikit-learn
scikit-learn
Datatron
Datatron

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

Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.

-
Explore models built and uploaded by your Data Science team, all from one centralized repository; Create and scale model deployments in just a few clicks. Deploy models developed in any framework or language; Make better business decisions to save your team time and money. Monitor model performance and detect model decay as it happens; Spend less time on model validation, bias detection, and internal audit processes. Go from model development to internal auditing to production faster than ever; Manage multivariate models through A/B testing for live inference and batch tasks; Apply business logic to your model prediction results. Create workflows for your models using multiple sources and languages
Statistics
GitHub Stars
63.9K
GitHub Stars
-
GitHub Forks
26.4K
GitHub Forks
-
Stacks
1.3K
Stacks
0
Followers
1.1K
Followers
10
Votes
45
Votes
0
Pros & Cons
Pros
  • 26
    Scientific computing
  • 19
    Easy
Cons
  • 2
    Limited
No community feedback yet
Integrations
No integrations available
TensorFlow
TensorFlow
H2O
H2O

What are some alternatives to scikit-learn, Datatron?

Heroku

Heroku

Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling.

Clever Cloud

Clever Cloud

Clever Cloud is a polyglot cloud application platform. The service helps developers to build applications with many languages and services, with auto-scaling features and a true pay-as-you-go pricing model.

Google App Engine

Google App Engine

Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.

Red Hat OpenShift

Red Hat OpenShift

OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.

AWS Elastic Beanstalk

AWS Elastic Beanstalk

Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.

Render

Render

Render is a unified platform to build and run all your apps and websites with free SSL, a global CDN, private networks and auto deploys from Git.

Hasura

Hasura

An open source GraphQL engine that deploys instant, realtime GraphQL APIs on any Postgres database.

TensorFlow

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.

Cloud 66

Cloud 66

Cloud 66 gives you everything you need to build, deploy and maintain your applications on any cloud, without the headache of dealing with "server stuff". Frameworks: Ruby on Rails, Node.js, Jamstack, Laravel, GoLang, and more.

Jelastic

Jelastic

Jelastic is a Multi-Cloud DevOps PaaS for ISVs, telcos, service providers and enterprises needing to speed up development, reduce cost of IT infrastructure, improve uptime and security.

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