Aquarium vs Microsoft Cognitive Services
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Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today. | Machine learning models are only as good as the datasets they're trained on. It helps ML teams make better models by improving their dataset quality. |
Build confidently with the first AI services to achieve human parity in computer vision, speech, and language;
Apply AI to more scenarios with the most comprehensive portfolio of domain-specific AI capabilities on the market;
Deploy anywhere from the cloud to the edge with containers | Upload your dataset to get a health check of its quality, quantity, and diversity. Zoom in and out of your dataset. Uncover distribution biases before you train. Find and fix labeling errors quickly;
Upload model inferences against your labeled datasets and deep dive into its performance. Find where your model is performing well and badly so you can take the best actions to improve it;
With knowledge of your dataset diversity and model performance, it automatically samples the best data to sample to label and retrain on. Your model performance just gets better |
Statistics | |
Stacks 52 | Stacks 9 |
Followers 34 | Followers 11 |
Votes 0 | Votes 0 |
What are some alternatives to Microsoft Cognitive Services, Aquarium?
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.
