Merlion vs ML Kit
Overview
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Detailed Comparison
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. | It is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting and anomaly detection for both univariate and multivariate time series. |
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 | Standardized and easily extensible data loading & benchmarking for a wide range of forecasting and anomaly detection datasets;
A library of diverse models for both anomaly detection and forecasting, unified under a shared interface. Models include classic statistical methods, tree ensembles, and deep learning approaches. Advanced users may fully configure each model as desired;
Abstract DefaultDetector and DefaultForecaster models that are efficient, robustly achieve good performance, and provide a starting point for new users;
AutoML for automated hyperaparameter tuning and model selection;
Practical, industry-inspired post-processing rules for anomaly detectors that make anomaly scores more interpretable, while also reducing the number of false positives |
Statistics | |
GitHub Stars - | GitHub Stars 4.4K |
GitHub Forks - | GitHub Forks 349 |
Stacks 137 | Stacks 2 |
Followers 209 | Followers 4 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to ML Kit, Merlion?
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.
