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Keras VS IBM Cloud Object Storage

Compare Keras VS IBM Cloud Object Storage 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.

IBM Cloud Object Storage logo IBM Cloud Object Storage

IBM Cloud Object Storage is a platform that offers cost-effective and scalable cloud storage for unstructured data.
  • Keras Landing page
    Landing page //
    2023-10-16
  • IBM Cloud Object Storage Landing page
    Landing page //
    2023-09-18

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.

IBM Cloud Object Storage features and specs

  • Scalability
    IBM Cloud Object Storage offers very high scalability, allowing businesses to store large amounts of data easily. This flexibility is crucial for businesses that are growing their storage needs or have fluctuating demands.
  • Data Resiliency
    The service provides robust data resiliency options, including geo-dispersed storage configurations, enabling enhanced protection against data loss and improved availability.
  • Cost Efficiency
    With its flexible pricing model, businesses can choose options that best fit their budget, such as 'Pay-as-you-go' plans, thereby optimizing costs according to actual usage.
  • Security Features
    It comes with comprehensive security features, including encryption, access control, and integration with IAM policies, ensuring that data is protected both at rest and in transit.
  • Integration
    Seamless integration with the broader IBM Cloud ecosystem, as well as other cloud services and applications, allows businesses to easily incorporate this storage solution into their existing cloud strategy.

Possible disadvantages of IBM Cloud Object Storage

  • Complexity
    The extensive feature set and customization options might lead to a steeper learning curve for new users or smaller teams without dedicated IT resources.
  • Performance Variability
    Depending on the region and specific use case, users might encounter variability in performance, particularly in scenarios requiring low-latency or high-throughput data access.
  • Support Availability
    While IBM offers various support plans, certain users might find the support mechanisms, such as community forums and basic plans, less responsive compared to some other providers.
  • Pricing Complexity
    Although pricing models are flexible, they can also become complex and convoluted, making it difficult for some businesses to predict costs precisely without detailed monitoring and analysis.
  • Limited Proprietary Tooling
    Compared to some competitors, IBM might have fewer proprietary tools and native applications directly integrated with their cloud storage, potentially requiring additional third-party tools or custom development for specific needs.

Keras videos

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

IBM Cloud Object Storage videos

IBM Cloud Object Storage: Built for business

More videos:

  • Review - Getting Started with IBM Cloud Object Storage
  • Review - IBM Cloud Object Storage webinar

Category Popularity

0-100% (relative to Keras and IBM Cloud Object Storage)
Data Science And Machine Learning
Cloud Computing
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Cloud Storage
0 0%
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 IBM Cloud Object Storage

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...

IBM Cloud Object Storage Reviews

We have no reviews of IBM Cloud Object Storage yet.
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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 / 16 days 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 / 7 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 / 11 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

IBM Cloud Object Storage mentions (0)

We have not tracked any mentions of IBM Cloud Object Storage yet. Tracking of IBM Cloud Object Storage recommendations started around Mar 2021.

What are some alternatives?

When comparing Keras and IBM Cloud Object Storage, 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.

Alibaba Object Storage Service - Alibaba Object Storage Service is an encrypted and secure cloud storage service which stores, processes and accesses massive amounts of data

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

Wasabi Cloud Object Storage - Storage made simple. Faster than Amazon's S3. Less expensive than Glacier.

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

Contabo Object Storage - S3-compatible cloud object storage with unlimited, free transfer at a fraction of what others charge. Easy migration & predictable billing. Sign up now & save.