Keras vs Datatron

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

Datatron

Stacks0

Followers10

Votes0

Keras vs Datatron: What are the differences?

Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. On the other hand, Datatron is detailed as "Production AI Model Management at Scale". Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.

Keras and Datatron can be categorized as "Machine Learning" tools.

Some of the features offered by Keras are:

neural networks API
Allows for easy and fast prototyping
Convolutional networks support

On the other hand, Datatron provides the following key features:

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

Keras is an open source tool with 46.7K GitHub stars and 17.7K GitHub forks. Here's a link to Keras's open source repository on GitHub.

🔥 Trending in Development & Training Tools on StackShare

Tinker AI Development Training Tools

Tinker

Try it View Docs Alternatives

Try

0

1

Advice on Keras, Datatron

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

Detailed Comparison

Keras	Datatron
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	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
Stacks 1.1K	Stacks 0
Followers 1.1K	Followers 10
Votes 22	Votes 0
Pros & Cons
Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug	No community feedback yet
Integrations
TensorFlow scikit-learn Python	TensorFlow scikit-learn H2O

What are some alternatives to Keras, Datatron?

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 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 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

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

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 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

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

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 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 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.