Keras vs ML Kit

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

ML Kit

Stacks137

Followers209

Votes0

Keras vs ML Kit: What are the differences?

Model Flexibility: Keras offers a high level of flexibility in building neural networks, allowing for customization of network architecture, layer configurations, and optimization algorithms, while ML Kit provides pre-trained models for specific tasks like image labeling and text recognition, limiting customization options.
Framework Dependency: Keras can be used with different backend frameworks like TensorFlow, Theano, and Microsoft Cognitive Toolkit, offering a wider range of options, whereas ML Kit is specifically designed to work with TensorFlow Lite, restricting the versatility in framework choices.
Platform Availability: Keras can be used on various platforms such as Windows, Linux, and macOS, providing flexibility for developers, whereas ML Kit is primarily for mobile platforms like Android and iOS, limiting its use on other operating systems.
Training Data: Keras requires developers to provide and manage their training data for model training, enabling complete control over the data used, whereas ML Kit uses pre-built datasets for its tasks, simplifying the development process but potentially limiting the accuracy of models due to lack of specialized training data.
Custom Model Integration: Keras allows for the integration of custom-built models and components into the neural network architecture, giving developers more control over the model's behavior, whereas ML Kit focuses on utilizing pre-built models and does not provide extensive support for incorporating custom models.
Development Environment: Keras can be integrated with popular IDEs like Jupyter Notebook and Google Colab, offering a familiar and feature-rich development environment, while ML Kit development is primarily done through Android Studio for Android apps and Xcode for iOS apps, requiring developers to adapt to these specific environments.

In Summary, Keras and ML Kit differ in model flexibility, framework dependencies, platform availability, training data requirements, custom model integration, and development environments.

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Advice on Keras, ML Kit

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

4.64k views4.64k

Comments

Fabian

Software Developer at DCSIL

Feb 11, 2021

Decided

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

55.4k views55.4k

Comments

Detailed Comparison

Keras	ML Kit
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	Image labeling - Identify objects, locations, activities, animal species, products, and more; Text recognition (OCR) - Recognize and extract text from images; Face detection - Detect faces and facial landmarks; Barcode scanning - Scan and process barcodes; Landmark detection - Identify popular landmarks in an image; Smart reply - Provide suggested text snippet that fits context
Statistics
Stacks 1.1K	Stacks 137
Followers 1.1K	Followers 209
Votes 22	Votes 0
Pros & Cons
Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug	No community feedback yet
Integrations
TensorFlow scikit-learn Python	No integrations available

What are some alternatives to Keras, ML Kit?

TensorFlow

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.

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.

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

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.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Related Comparisons

Keras vs ML Kit: What are the differences?

Model Flexibility: Keras offers a high level of flexibility in building neural networks, allowing for customization of network architecture, layer configurations, and optimization algorithms, while ML Kit provides pre-trained models for specific tasks like image labeling and text recognition, limiting customization options.
Framework Dependency: Keras can be used with different backend frameworks like TensorFlow, Theano, and Microsoft Cognitive Toolkit, offering a wider range of options, whereas ML Kit is specifically designed to work with TensorFlow Lite, restricting the versatility in framework choices.
Platform Availability: Keras can be used on various platforms such as Windows, Linux, and macOS, providing flexibility for developers, whereas ML Kit is primarily for mobile platforms like Android and iOS, limiting its use on other operating systems.
Training Data: Keras requires developers to provide and manage their training data for model training, enabling complete control over the data used, whereas ML Kit uses pre-built datasets for its tasks, simplifying the development process but potentially limiting the accuracy of models due to lack of specialized training data.
Custom Model Integration: Keras allows for the integration of custom-built models and components into the neural network architecture, giving developers more control over the model's behavior, whereas ML Kit focuses on utilizing pre-built models and does not provide extensive support for incorporating custom models.
Development Environment: Keras can be integrated with popular IDEs like Jupyter Notebook and Google Colab, offering a familiar and feature-rich development environment, while ML Kit development is primarily done through Android Studio for Android apps and Xcode for iOS apps, requiring developers to adapt to these specific environments.

In Summary, Keras and ML Kit differ in model flexibility, framework dependencies, platform availability, training data requirements, custom model integration, and development environments.

Keras vs ML Kit

Overview