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Appian VS TensorFlow

Compare Appian VS TensorFlow and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Appian logo Appian

See how Appian, leading provider of modern low-code and BPM software solutions, has helped transform the businesses of over 3.5 million users worldwide.

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.
  • Appian Landing page
    Landing page //
    2023-10-20
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Appian features and specs

  • Low-Code Development
    Appian allows users to create applications with minimal hand-coding, catering to business analysts and developers alike. Its drag-and-drop interface simplifies development and accelerates time-to-market.
  • Process Automation
    Appian excels in automating complex business processes, improving operational efficiency, and reducing human error. Its powerful BPM tools streamline workflows effectively.
  • Integration Capabilities
    Appian provides strong integration capabilities with various third-party systems, databases, and cloud services. This ensures that applications can seamlessly communicate with existing enterprise systems.
  • Enterprise-Grade Security
    Appian offers robust security features, including role-based access control and data encryption, making it suitable for businesses with stringent security requirements.
  • Scalability
    As a cloud-native platform, Appian is highly scalable, supporting the needs of growing enterprises by easily handling increased loads and more complex applications.
  • User Experience
    The platform provides a user-friendly interface, both for developers building the applications and end-users interacting with them, enhancing overall user satisfaction.

Possible disadvantages of Appian

  • Cost
    Appian can be expensive, particularly for small to medium-sized businesses. Its pricing model might not be feasible for organizations operating on a limited budget.
  • Learning Curve
    Although it simplifies development, mastering Appian still requires a learning curve. Users need to invest time in training, which can slow down the initial development phase.
  • Complex Customization
    Highly tailored or very specific customizations can be challenging to implement within Appian. Some complicated functionalities may require extensive workarounds.
  • Limited Offline Functionality
    Appian's offline capabilities are limited, which can be a disadvantage for field services or users who need to access the application without a reliable internet connection.
  • Vendor Lock-In
    Due to its proprietary technology, organizations may face vendor lock-in, making it challenging to migrate applications or data to another platform if needed.
  • Performance Issues at Scale
    While Appian is scalable, some users report performance issues when running extremely large and complex applications, which can impact user experience and overall efficiency.

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.

Appian videos

+ Add

Appian CEO: Delivering โ€˜Mission-Criticalโ€™ Software | Mad Money | CNBC

More videos:

  • Review - This is Appian
  • Review - Appian Application Designer: Build Applications in Days, not Years

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 Appian and TensorFlow)

Appian

TensorFlow

BPM
100 100%
BPM
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

Share your experience with using Appian and TensorFlow. For example, how are they different and which one is better?
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Reviews

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

Appian Reviews

Top 10 Microsoft Power Apps Alternatives and Competitors 2024
Strengths: A powerful low-code platform, Appian caters to large enterprises with complex business process automation needs. It offers robust workflow management capabilities, allowing you to automate intricate business processes with decision-making logic and case management features. Appian excels in industries like finance, insurance, and healthcare, where complex...
Source: medium.com
7 Best Business Process Management Tools (2023)
Appian is used by companies to automate their routine tasks, integrate with other enterprise tools, and create custom workflows. It offers a wide range of features such as mobile app development, chatbots, AI assistants, etc.
Source: theecmconsultant.com
11 Business Process Management (BPM) Software for SMBs
Experience the power of business workflow automation with Appian and accelerate your business governance, results, and efficiency. Appian simplifies your workflow design to empower users and pro developers to draw processes, such as a flowchart.
Source: geekflare.com
10 Best Low-Code Development Platforms in 2020
Verdict: Appian is the provider of the software development platform. The Appian low code development platform is a combination of intelligent automation and low-code development.
Source: www.softwaretestinghelp.com
Best Google App Maker alternatives in 2020
Appian excels when used to create form-based apps. The product does allow building custom UI components, but only by using Appianโ€™s in-product customization tools. Appianโ€™s reasoning for this is security and to ensure JS compliance across browsers and devices.
Source: retool.com

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 Franรงois 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 Deepmindโ€™s Acme framework is implemented in TensorFlow. OpenAIโ€™s Baselines model repository is also implemented in TensorFlow, although OpenAIโ€™s Gym can be...
Source: www.assemblyai.com

Social recommendations and mentions

TensorFlow might be a bit more popular than Appian. We know about 8 links to it since March 2021 and only 7 links to Appian. 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.

Appian mentions (7)

  • AI Coding Adoption at Enterprise Scale Is Harder Than Anyone Admits
    AI coding adoption at enterprise scale is hard because the real project is not installing a tool. It is redesigning trust, review, ownership, and delivery discipline around a new source of code generation. That's where platforms like Retool, ToolJet, Appian, etc. shine. - Source: dev.to / 4 months ago
  • Enterprise App Builders That Are Actually Enterprise-Ready (Top 5)
    You are process-heavy and regulated, and your app is basically a workflow engine: Appian. - Source: dev.to / 5 months ago
  • Low-Code tools & Frontend
    Does any of you use a low-code tool like Retool or Appian? If so, what is the most common use case? Source: over 3 years ago
  • Appian Associate Developer Certification help
    Look for use case inspiration in the Solutions area of appian.com and within the AppMarket. See if you can build proof of concepts of some of these. Source: over 3 years ago
  • What is best way to create an online responsive database driven website to help manage my small orchard?
    There are low code database driven website creation systems out there at the moment e.g. OutSystems and Appian however they have very limited free trials (e.g. auto-disable after a few days of no use), and then the paid options are again too expensive. Although I will note that they seem to be great in terms of their usability and would be perfect for creating a simple interface without too much diving into code. Source: almost 4 years ago
View more

TensorFlow mentions (8)

  • Why 70% of Americans See AI as a Wealth Inequality Machine: The Developer's Role in Building Fairer Tech
    The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 4 months ago
  • 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 3 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 4 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 4 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: over 4 years ago
View more

What are some alternatives?

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

Camunda - The Universal Process Orchestrator

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

Kintone - Build business apps and supercharge your company's productivity with kintone's all-in-one...

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

Bizagi - Bizagi is a Business Process Management (BPMS) solution for faster and flexible process automation. It's powerful yet intuitive BPM Suite is designed to make your business more agile.

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

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