Overview

WalkMe

Stacks24

Followers72

Votes0

TensorFlow

Stacks3.8K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

rasa NLU

Stacks121

Followers282

Votes25

TensorFlow vs WalkMe vs rasa NLU: What are the differences?

<Write Introduction here>

1. **Language Support**: TensorFlow supports multiple programming languages like Python, C++, and Java, while WalkMe and Rasa NLU are primarily focused on Python for development.
2. **Application Focus**: WalkMe is a customer experience platform used for creating interactive on-screen guidance, while TensorFlow is a machine learning library focusing on dataflow and differentiable programming. Rasa NLU, on the other hand, is a natural language understanding tool specifically designed for conversational AI applications. 
3. **Popularity and Community**: TensorFlow has a larger community and extensive documentation compared to WalkMe and Rasa NLU, which may result in more easily accessible resources and support for developers.
4. **Ease of Use**: WalkMe provides a user-friendly interface for creating on-screen walkthroughs without the need for coding, while TensorFlow and Rasa NLU require some level of programming skills for implementation and customization.
5. **Third-Party Integration**: Rasa NLU integrates well with different chatbot platforms, enabling developers to create chatbot applications with ease. TensorFlow also offers various integration options, but with a more general focus on machine learning applications.
6. **Cost Consideration**: WalkMe is a commercial product, requiring a subscription to access its full range of features, whereas both TensorFlow and Rasa NLU are open-source tools that can be used freely without any licensing costs.

In Summary, The key differences between TensorFlow, WalkMe, and Rasa NLU lie in their language support, application focus, popularity, ease of use, third-party integration, and cost consideration. Each tool offers unique features tailored to specific development needs.

Advice on WalkMe, TensorFlow, rasa NLU

Xi

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.63k views4.63k

Comments

Detailed Comparison

WalkMe	TensorFlow	rasa NLU
WalkMe enables website owners and app developers to easily create multiple interactive on-screen Walk-Thru’s that help users to quickly and easily complete even the most complex tasks.	TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.	rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
Using WalkMe's advanced Editor, you can create Walk-Thrus within minutes, without any technical knowledge, all by using a simple point-and-click interface.;WalkMe supports all major browsers.;Welcome Screen- Customize your welcome screen to greet your user to your Walk-Thru;Auto Start- Automatically play your Walk-Thru upon site entrance;Redirect- Redirect your user’s browser to any URL in your website;Skip First Step- Guide users directly to your second step, instantly;Branched Walk-Thrus- Using the branching option, WalkMe allows you to create branched Walk-Thrus that can cover any possible business process.;Customized Balloons and Player;Create your Walk-Thrus in numerous languages;Full API- WalkMe delivers a full API, which allows you to utilize your imagination. Play your Walk-Thrus using your own buttons or links.;Balloon Position- Set your instruction balloons for your Walk-Thru in different locations on the website.;Timing Options- Customize the amount of time you would like to delay and display your step during the Walk-Thru playback.;Skippable- Allow your users to skip steps without harming the Walk-Thru process.;Advanced Settings- Set your Walk-Thru to seek out specific UI elements associated with a specific step according to specific parameters.;Step Triggers- In order to support any possible business process, the WalkMe Editor delivers a set of step triggers, which allows you to specify when a step is finished and when it is time to advance to the next step.;Analytics- WalkMe allows you to track your Walk-Thru statistics with its Analytics platform	-	Open source; NLP; Machine learning
Statistics
GitHub Stars -	GitHub Stars 192.3K	GitHub Stars -
GitHub Forks -	GitHub Forks 74.9K	GitHub Forks -
Stacks 24	Stacks 3.8K	Stacks 121
Followers 72	Followers 3.5K	Followers 282
Votes 0	Votes 106	Votes 25
Pros & Cons
No community feedback yet	Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 9 Open Source 6 Docker Image 6 Self Hosted 3 Comes with rasa_core 1 Enterprise Ready Cons 4 Wdfsdf 4 No interface provided
Integrations
No integrations available	JavaScript	Slack RocketChat Google Hangouts Chat Telegram Microsoft Bot Framework Twilio Mattermost

What are some alternatives to WalkMe, TensorFlow, rasa NLU?

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

SpaCy

It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.