KNIME VS TensorFlow

Compare KNIME VS TensorFlow and see what are their differences

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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.

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2023-09-28

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2023-06-19

KNIME

Website: knime.com
Pricing URL: Official KNIME Pricing
$ Details: -
Startup details
Country: Switzerland
City: Zürich
Employees: 50 - 99

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TensorFlow

Website: tensorflow.org
Pricing URL: -
$ Details

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KNIME features and specs

User-Friendly Interface
KNIME provides a visual workflow interface that makes it easy for users to design data processing, analysis, and machine learning workflows without needing to write code.
Extensibility
KNIME supports various extensions and plugins, which enhance its functionality and allow integration with different data sources, tools, and programming languages like R and Python.
Open Source
KNIME offers an open-source platform, which means users can access and modify the source code, contributing to its flexibility and cost-effectiveness.
Robust Community Support
A strong community of users and developers around KNIME provides extensive documentation, forums, and shared workflows to help solve issues and improve the platform.
Scalability
KNIME can handle large volumes of data and complex workflows, making it scalable for both small projects and large enterprise solutions.

Possible disadvantages of KNIME

Learning Curve
While the interface is user-friendly, new users may initially find it challenging to understand all the features and capabilities, leading to a significant learning curve.
Performance
For extremely large datasets or very complex workflows, KNIME can exhibit performance issues, including slower processing speeds and higher memory consumption.
Limited Advanced Machine Learning Capabilities
While KNIME is powerful for basic and intermediate analytics, it may lack some of the advanced machine learning capabilities found in specialized tools like TensorFlow or PyTorch.
Dependency on Extensions
A lot of KNIME’s advanced functionality relies on external extensions, which may not always be well-maintained or compatible with newer versions.
Commercial Licensing Costs
While the core platform is open-source, advanced features, support, and enterprise-level tools require a commercial license, which can be costly.

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.

KNIME videos

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What Is KNIME?

TensorFlow videos

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

Category Popularity

0-100% (relative to KNIME and TensorFlow)

KNIME

TensorFlow

Data Science And Machine Learning

15 15%

Data Science And Machine Learning

85% 85

Business & Commerce

100 100%

Business & Commerce

0% 0

0 0%

100% 100

Technical Computing

100 100%

Technical Computing

0% 0

User comments

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

Reviews

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

KNIME Reviews

Top 10 Tableau Open Source Alternatives: A Comprehensive List

Knime Analytics Platform is an open-source Business Intelligence software that has been developed as an integration platform for creating analytical reports. It is a software that might be difficult for a novice to use. However, for Data Scientists and other Data professionals, particularly those who want to work with R, Python, or other Predictive Machine Learning tools,...

Source: hevodata.com

Top 10 Data Analysis Tools in 2022

KNIME KNIME is an open-source tool that allows you to build or manipulate software to fit your company goals. KNIME is a free data analysis tool. KNIME is a valuable tool that is freely accessible and can be modified due to its open architecture. However, there is a paucity of learning materials and a need for better visualization.

Source: www.spiceworks.com

15 data science tools to consider using in 2021

Some platforms are also available in free open source or community editions -- examples include Dataiku and H2O. Knime combines an open source analytics platform with a commercial Knime Server software package that supports team-based collaboration and workflow automation, deployment and management.

Source: searchbusinessanalytics.techtarget.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 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 KNIME. 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.

KNIME mentions (2)

Replace SAP BI with what?
I'd recommend to look into the free and open source KNIME tool (knime.com). It may not look easy to use right away, but if you stick with it for a little while and attend its learning guides, KNIME will grow on you. You can even have it scheduled using Microsoft Task Scheduler or CRON for free. For me, it has augmented the capabilities of Power BI, Looker Studio, Cognos, Excel, and other proprietary tools. Its... Source: almost 2 years ago
More "pythonic" way of writing my API query?
That would cause a problem because ultimately this query will be scheduled to run multiple times a day on a KNIME server. Source: about 2 years ago

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 / about 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: almost 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