GraphPipe vs ML Kit
Overview
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CLI (Node.js)
Manual
Detailed Comparison
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. | GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations. |
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 | A minimalist machine learning transport specification based on flatbuffers;
Simple, efficient reference model servers for Tensorflow, Caffe2, and ONNX.;
Efficient client implementations in Go, Python, and Java. |
Statistics | |
GitHub Stars - | GitHub Stars 718 |
GitHub Forks - | GitHub Forks 103 |
Stacks 137 | Stacks 2 |
Followers 209 | Followers 16 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to ML Kit, GraphPipe?
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.
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
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.
