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Medallia VS TensorFlow

Compare Medallia VS TensorFlow and see what are their differences

Electe
Discover Electe, our data analytics platform dedicated to SMEs. Don't let your data go unused, take your business into the future! featured
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Medallia logo Medallia

Medallia enables companies to capture customer feedback, understand it in real-time, and take action to improve the customer experience (CX).

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.
  • Medallia Landing page
    Landing page //
    2023-10-17

  www.medallia.comSoftware by Medallia

  • TensorFlow Landing page
    Landing page //
    2023-06-19

Medallia features and specs

  • Comprehensive customer feedback collection
    Medallia provides robust tools for collecting feedback from various channels including web, mobile, email, social media, and in-store, allowing for a holistic view of customer sentiments.
  • Advanced analytics and reporting
    The platform offers advanced analytics and reporting features, which help businesses to derive insights and track performance metrics, making data-driven decision-making more accessible.
  • Customizable dashboards
    Medallia allows users to create customizable dashboards to suit specific business needs and preferences, facilitating easier data visualization and monitoring.
  • Real-time feedback and alerts
    Medallia provides real-time feedback and alert capabilities, enabling companies to address issues promptly and improve customer experience in a timely manner.
  • Integration capabilities
    The platform can integrate seamlessly with other business systems and tools, such as CRM systems, which helps streamline operations and enhance data connectivity.
  • Ease of Use
    MonkeyLearn provides an intuitive and user-friendly interface that allows even non-technical users to create, train, and deploy machine learning models with ease.
  • No Coding Required
    Users can build and train models without the need for programming skills, which makes it accessible for individuals and teams without a technical background.
  • Pre-Built Models
    MonkeyLearn offers a variety of pre-trained models for tasks like sentiment analysis, keyword extraction, and topic classification, which can save time and effort.
  • Scalability
    MonkeyLearn can scale with your needs, allowing businesses of various sizes to handle different volumes of data efficiently.
  • Real-Time Analysis
    The platform supports real-time text analysis, which can be particularly beneficial for applications requiring immediate insights.

Possible disadvantages of Medallia

  • High cost
    Medallia can be expensive, particularly for small to medium-sized businesses, which might find the pricing model prohibitive.
  • Complex setup
    The initial setup and implementation process can be complex and time-consuming, often requiring expert assistance and detailed planning.
  • Steep learning curve
    Due to its extensive features and functionalities, new users might experience a steep learning curve and might need additional training to fully utilize the platform.
  • Customization limitations
    While Medallia offers customization options, some users have reported limitations and restrictions in tailoring the system to their specific needs beyond what is provided out-of-the-box.
  • Dependency on internet connectivity
    As a cloud-based solution, Medallia's performance is highly dependent on reliable internet connectivity, which can be a drawback in areas with poor internet infrastructure.
  • Cost
    While MonkeyLearn offers a free tier, advanced features and higher usage limits can become costly, which might be a barrier for small businesses or individual users.
  • Limited Customization
    Although the platform is easy to use, the level of customization for models might be limited compared to more advanced machine learning frameworks.
  • Dependency on Platform
    Users become reliant on MonkeyLearn’s infrastructure and updates, which may pose risks if any changes or issues arise on their end.
  • Data Privacy Concerns
    As with any SaaS platform, there are considerations around data privacy and security, especially for businesses handling sensitive information.
  • Technical Limitations
    MonkeyLearn may not be suitable for highly complex or specialized machine learning tasks that require in-depth customization and fine-tuning.
  • Learning Curve for Advanced Features
    While basic functionalities are easy to grasp, more advanced features may still require some learning and practice, especially for users who are entirely new to machine learning.

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.

Medallia videos

+ Add

Medallia Experience Cloud in Action

More videos:

  • Review - Zapier + MonkeyLearn integration
  • Review - Analyzing Customer Reviews with MonkeyLearn and RapidMiner
  • Review - Medallia for Retail: Solution Overview
  • Review - Webinar - Introduction to MonkeyLearn
  • Review - Making a Custom Text Classifier with MonkeyLearn
  • Review - Medallia for B2B: Solution Overview

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 Medallia and TensorFlow)

Medallia

TensorFlow

Surveys
100 100%
Surveys
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
User Feedback
100 100%
User Feedback
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Medallia 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 Medallia and TensorFlow

Medallia Reviews

Top 10 AI Data Analysis Tools in 2024
MonkeyLearn is a specialized AI data analysis tool that focuses on text analysis. It offers a suite of AI-driven tools adept at analyzing, categorizing, and visualizing text data, all tailored to user-defined parameters. This platform is particularly valuable for organizations that need in-depth analysis of textual data, such as customer feedback, social media content, and...
Source: powerdrill.ai
10 Better Alternatives to Survey Monkey for Comprehensive Data Collection
Medallia is a compelling alternative to Survey Monkey, especially for enterprises looking to gain a comprehensive understanding of customer experiences and feedback. Its extensive feedback collection options, advanced analytics, and focus on actionable insights make it an invaluable tool for businesses striving to enhance customer satisfaction and loyalty. While Medallia...
Source: www.zoho.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 Medallia. 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.

Medallia mentions (1)

  • Best AI SEO Tools for NLP Content Optimization
    MonkeyLearn: A platform for text analysis and machine learning, allowing users to train custom models for tasks like sentiment analysis and topic classification. Source: over 1 year ago
  • Best 10 AI Tools for Google Sheets (2023)
    MonkeyLearn: MonkeyLearn is a powerful AI tool that automates text tagging in Google Sheets, eliminating manual and repetitive tasks. It is 100 times faster than human processing, significantly saving time, and 50 times more cost-effective. With MonkeyLearn, users can ensure consistent tagging criteria without errors, enabling efficient analysis of spreadsheets and faster insights from data. It offers direct... Source: almost 2 years ago
  • free-for.dev
    Monkeylearn.com — Text analysis with machine learning, free 300 queries/month. - Source: dev.to / over 2 years ago
  • [D] What are the best SaaS APIs for non-English NLP tasks?
    MonkeyLearn supports 11 languages for data analysis (Spanish, Portuguese, German, Russian, Italian, French, Dutch, Chinese, Japanese, Korean and Arabic).  But for sentiment analysis, only Spanish seems to be available, I’m not sure about that. Source: over 2 years ago
  • Word Cloud From This Sub [OC]
    R3: Used RedditExtractoR in R to download all-time top posts, and ran the resulting .csv through https://monkeylearn.com/. Downloaded the resulting table and deleted top result "OC" - then visualized it with ggplot to give a sense of absolute numbers. Total posts considered in this are 988, the word cloud only looks at the 98 most mentioned words/phrases. Let me know if you have got any questions/concerns! Source: almost 3 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 / 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: 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
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What are some alternatives?

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

Qualtrics - Qualtrics is the most trusted research platform, helping brands make crucial business decisions. From surveys to insights to action.

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

Wootric - Wootric is software that allows apps and websites to take customer satisfaction surveys so that you can properly gauge the popularity and success of your app through the eyes of the people using it. Read more about Wootric.

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

Qualtrics Customer Experience - Qualtrics Customer Experience software makes it easy to monitor, respond & improve every key moment along the customer journey. Request a demo today!

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

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