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Keras VS Codeship

Compare Keras VS Codeship and see what are their differences

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Keras logo Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Codeship logo Codeship

Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.
  • Keras Landing page
    Landing page //
    2023-10-16
  • Codeship Landing page
    Landing page //
    2023-10-19

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

Codeship features and specs

  • Ease of Use
    Codeship offers an intuitive interface that simplifies the setup process, making it accessible for developers who may not be experienced with continuous integration (CI) and continuous deployment (CD) tools.
  • Integration with Cloud Services
    Codeship integrates seamlessly with cloud services such as AWS, Google Cloud, and Heroku, facilitating easy deployment of applications.
  • Flexible Workflows
    The tool provides support for both Codeship Basic and Codeship Pro, allowing for flexibility in choosing between a more straightforward or a more customizable CI/CD workflow.
  • Docker Support
    Codeship Pro offers extensive support for Docker, allowing developers to use containerization strategies for their build and deployment processes.
  • Parallel Test Pipelines
    It supports parallel test pipelines, which can significantly speed up the testing process and reduce build times.
  • Slack Integration
    Codeship integrates with communication tools like Slack, enabling notifications and updates directly within team communication channels.

Possible disadvantages of Codeship

  • Cost
    Codeship can be more expensive compared to other CI/CD tools, particularly for larger teams or more complex projects that require more build resources.
  • Limited Customization
    For highly customized CI/CD processes, Codeship Basic might be limiting. Users may need to switch to Codeship Pro, which requires more configuration and a steeper learning curve.
  • Performance Bottlenecks
    Users have reported occasional performance bottlenecks, particularly under heavy workloads, which can slow down the CI/CD pipeline.
  • Plugin Ecosystem
    The plugin ecosystem for Codeship is not as extensive as some other CI/CD tools like Jenkins, potentially limiting its integration capabilities.
  • Learning Curve
    While Codeship Basic is relatively easy to use, Codeship Pro has a steeper learning curve, particularly for users who are new to Docker and advanced CI/CD practices.
  • Support
    Although support is available, some users have reported slower response times and less comprehensive support compared to other CI/CD platforms.

Analysis of Keras

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

Keras videos

+ Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

Codeship videos

+ Add

LinuxFest Northwest 2017: Continuous Delivery to Microsoft Azure with Docker through Codeship

More videos:

  • Review - The Codeship -- Continuous Deployment made simple

Category Popularity

0-100% (relative to Keras and Codeship)

Keras

Codeship

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
DevOps Tools
0 0%
DevOps Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Continuous Integration
0 0%
Continuous Integration
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Keras and Codeship

Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

Codeship Reviews

The Best Alternatives to Jenkins for Developers
Codeship, a CI/CD platform based in the cloud, has an interface that is easy for users and it can integrate with numerous tools and services people are familiar with. It works well for different programming languages and platforms, which makes it suitable for many teams involved in development work.
Source: morninglif.com
Top 10 Most Popular Jenkins Alternatives for DevOps in 2024
CodeShip is a CloudBees SaaS platform that provides a managed CI/CD experience in the cloud. It’s designed to give control back to developers by providing a guided workflow for creating and maintaining CI/CD pipelines. This avoids much of the complexity that’s associated with Jenkins.
Source: spacelift.io
10 Jenkins Alternatives in 2021 for Developers
You could consider using CodeShip to help you to optimize CI/CD cloud deployment. CodeShip can be used by just about any type of development team that looks to increase the efficiency and automation of their code delivery. You can get started within minutes and gain access to an incredible amount of control when setting everything up. The customization options will seem...
Source: solutionsuggest.com
The Best Alternatives to Jenkins for Developers
CodeShip is a hosted continuous integration and continuous delivery platform found by CloudBees. It provides fast feedback and customized environments to build applications. It provides integration with almost anything and is good at helping you scale as per your needs. It comes free for up to 100 monthly builds.
Source: www.lambdatest.com

Social recommendations and mentions

Based on our record, Keras seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / about 1 month ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 12 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
View more

Codeship mentions (0)

We have not tracked any mentions of Codeship yet. Tracking of Codeship recommendations started around Mar 2021.

What are some alternatives?

When comparing Keras and Codeship, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Travis CI - Simple, flexible, trustworthy CI/CD tools. Join hundreds of thousands who define tests and deployments in minutes, then scale up simply with parallel or multi-environment builds using Travis CI’s precision syntax—all with the developer in mind.

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