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Azkaban VS TensorFlow

Compare Azkaban VS TensorFlow and see what are their differences

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Azkaban logo Azkaban

Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

TensorFlow logo 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.
  • Azkaban Landing page
    Landing page //
    2019-08-01
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Azkaban features and specs

  • Scalability
    Azkaban is designed to efficiently manage and schedule batch jobs, making it suitable for handling large-scale data processing tasks in a distributed environment.
  • Dependency Management
    Azkaban offers robust dependency management, allowing complex job workflows with dependencies to be easily orchestrated and visualized.
  • Web-Based Interface
    It provides a user-friendly web interface for managing workflows, monitoring job execution, and handling configurations, which enhances user interaction.
  • Open Source
    As an open-source tool, Azkaban allows for customization and community contributions, which can lead to rapid feature enhancements and bug fixes.
  • Integration
    Azkaban integrates well with other Hadoop ecosystem tools, making it an excellent choice for big data environments.

Possible disadvantages of Azkaban

  • Complex Setup
    Setting up and configuring Azkaban can be complex and time-consuming, requiring detailed knowledge of its system and dependencies.
  • Limited Support
    Being an open-source project, it might not have the extensive enterprise-level support that commercial workflow management tools offer.
  • Resource Management
    Azkaban lacks advanced resource management capabilities found in some other orchestration tools, which can be a limitation in environments with diverse resource needs.
  • Interface Limitations
    While it does provide a web-based interface, some users find it less intuitive compared to those of other modern workflow management systems.
  • Feature Set
    Azkaban may lack some advanced features and integrations available in newer orchestration tools, which could be limiting for certain complex or evolving needs.

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

Azkaban videos

+ Add

Harry Potter and the Prisoner of Azkaban - Movie Review

More videos:

  • Review - Harry Potter and the Prisoner of Azkaban - Movie Review
  • Review - Harry Potter and The Prisoner of Azkaban

TensorFlow videos

+ Add

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

Category Popularity

0-100% (relative to Azkaban and TensorFlow)

Azkaban

TensorFlow

DevOps Tools
100 100%
DevOps Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Workflow Automation
100 100%
Workflow Automation
0% 0
AI
0 0%
AI
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 Azkaban and TensorFlow

Azkaban Reviews

We have no reviews of Azkaban yet.
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TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
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
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...
Source: www.assemblyai.com

Social recommendations and mentions

Based on our record, TensorFlow should be more popular than Azkaban. It has been mentiond 7 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.

Azkaban mentions (3)

TensorFlow mentions (7)

  • Creating Image Frames from Videos for Deep Learning Models
    Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 2 years ago
  • Need help with a Tensorflow function
    So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 3 years ago
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
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What are some alternatives?

When comparing Azkaban and TensorFlow, you can also consider the following products

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

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

RunDeck - RunDeck is an open source automation service with a web console, command line tools and a WebAPI.

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

Metaflow - Framework for real-life data science; build, improve, and operate end-to-end workflows.

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

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